@conference {12444,
	title = {Face tracking in low resolution videos under illumination variations},
	booktitle = {2011 18th IEEE International Conference on Image Processing (ICIP)},
	year = {2011},
	month = {2011/09/11/14},
	pages = {781 - 784},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {In practical face tracking applications, the face region is often small and affected by illumination variations. We address this problem by using a new feature, namely the Gradient-Logarithmic Field (GLF) feature, in the particle filter framework. The GLF feature is robust under illumination variations and the GLF-based tracker does not assume any model for the face being tracked and is effective in low-resolution video. Experimental results show that the proposed GFL-based tracker works well under significant illumination changes and outperforms some of the state-of-the-art algorithms.},
	keywords = {Adaptation models, Computational modeling, Face, face recognition, face tracking, GLF-based tracker, gradient methods, gradient-logarithmic field feature, illumination variations, lighting, low resolution videos, low-resolution, particle filter, particle filter framework, particle filtering (numerical methods), Robustness, tracking, video signal processing, Videos, Visual face tracking},
	isbn = {978-1-4577-1304-0},
	doi = {10.1109/ICIP.2011.6116672},
	author = {Zou, W.W.W. and Chellapa, Rama and Yuen, P.C.}
}
@article {12476,
	title = {Online Empirical Evaluation of Tracking Algorithms},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {32},
	year = {2010},
	month = {2010/08//},
	pages = {1443 - 1458},
	abstract = {Evaluation of tracking algorithms in the absence of ground truth is a challenging problem. There exist a variety of approaches for this problem, ranging from formal model validation techniques to heuristics that look for mismatches between track properties and the observed data. However, few of these methods scale up to the task of visual tracking, where the models are usually nonlinear and complex and typically lie in a high-dimensional space. Further, scenarios that cause track failures and/or poor tracking performance are also quite diverse for the visual tracking problem. In this paper, we propose an online performance evaluation strategy for tracking systems based on particle filters using a time-reversed Markov chain. The key intuition of our proposed methodology relies on the time-reversible nature of physical motion exhibited by most objects, which in turn should be possessed by a good tracker. In the presence of tracking failures due to occlusion, low SNR, or modeling errors, this reversible nature of the tracker is violated. We use this property for detection of track failures. To evaluate the performance of the tracker at time instant t, we use the posterior of the tracking algorithm to initialize a time-reversed Markov chain. We compute the posterior density of track parameters at the starting time t = 0 by filtering back in time to the initial time instant. The distance between the posterior density of the time-reversed chain (at t = 0) and the prior density used to initialize the tracking algorithm forms the decision statistic for evaluation. It is observed that when the data are generated by the underlying models, the decision statistic takes a low value. We provide a thorough experimental analysis of the evaluation methodology. Specifically, we demonstrate the effectiveness of our approach for tackling common challenges such as occlusion, pose, and illumination changes and provide the Receiver Operating Characteristic (ROC) curves. Finally, we also s how the applicability of the core ideas of the paper to other tracking algorithms such as the Kanade-Lucas-Tomasi (KLT) feature tracker and the mean-shift tracker.},
	keywords = {Back, Biomedical imaging, Computer vision, Filtering, formal model validation techniques, formal verification, ground truth, Kanade Lucas Tomasi feature tracker, Karhunen-Loeve transforms, lighting, Markov processes, mean shift tracker, model validation., online empirical evaluation, particle filtering (numerical methods), Particle filters, Particle tracking, performance evaluation, receiver operating characteristic curves, Robustness, SNR, Statistics, Surveillance, time reversed Markov chain, tracking, tracking algorithms, visual tracking},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2009.135},
	author = {Wu,Hao and Sankaranarayanan,A. C and Chellapa, Rama}
}
@conference {12480,
	title = {Tracking via object reflectance using a hyperspectral video camera},
	booktitle = {2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
	year = {2010},
	month = {2010/06/13/18},
	pages = {44 - 51},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Recent advances in electronics and sensor design have enabled the development of a hyperspectral video camera that can capture hyperspectral datacubes at near video rates. The sensor offers the potential for novel and robust methods for surveillance by combining methods from computer vision and hyperspectral image analysis. Here, we focus on the problem of tracking objects through challenging conditions, such as rapid illumination and pose changes, occlusions, and in the presence of confusers. A new framework that incorporates radiative transfer theory to estimate object reflectance and the mean shift algorithm to simultaneously track the object based on its reflectance spectra is proposed. The combination of spectral detection and motion prediction enables the tracker to be robust against abrupt motions, and facilitate fast convergence of the mean shift tracker. In addition, the system achieves good computational efficiency by using random projection to reduce spectral dimension. The tracker has been evaluated on real hyperspectral video data.},
	keywords = {Computer vision, electronic design, hyperspectral datacubes, hyperspectral image analysis, Hyperspectral imaging, Hyperspectral sensors, hyperspectral video camera, Image motion analysis, Image sensors, lighting, Motion estimation, motion prediction, Object detection, object reflectance tracking, random projection, Reflectivity, robust methods, Robustness, sensor design, spectral detection, Surveillance, tracking, Video surveillance},
	isbn = {978-1-4244-7029-7},
	doi = {10.1109/CVPRW.2010.5543780},
	author = {Nguyen,Hien Van and Banerjee, A. and Chellapa, Rama}
}
@article {12513,
	title = {Multicamera Tracking of Articulated Human Motion Using Shape and Motion Cues},
	journal = {Image Processing, IEEE Transactions on},
	volume = {18},
	year = {2009},
	month = {2009/09//},
	pages = {2114 - 2126},
	abstract = {We present a completely automatic algorithm for initializing and tracking the articulated motion of humans using image sequences obtained from multiple cameras. A detailed articulated human body model composed of sixteen rigid segments that allows both translation and rotation at joints is used. Voxel data of the subject obtained from the images is segmented into the different articulated chains using Laplacian eigenmaps. The segmented chains are registered in a subset of the frames using a single-frame registration technique and subsequently used to initialize the pose in the sequence. A temporal registration method is proposed to identify the partially segmented or unregistered articulated chains in the remaining frames in the sequence. The proposed tracker uses motion cues such as pixel displacement as well as 2-D and 3-D shape cues such as silhouettes, motion residue, and skeleton curves. The tracking algorithm consists of a predictor that uses motion cues and a corrector that uses shape cues. The use of complementary cues in the tracking alleviates the twin problems of drift and convergence to local minima. The use of multiple cameras also allows us to deal with the problems due to self-occlusion and kinematic singularity. We present tracking results on sequences with different kinds of motion to illustrate the effectiveness of our approach. The pose of the subject is correctly tracked for the duration of the sequence as can be verified by inspection.},
	keywords = {2D shape cues, 3D shape cues, algorithms, Anatomic;Models, articulated human motion, automatic algorithm, Biological;Movement;Posture;Skeleton;Video Recording;, Computer-Assisted;Models, Eigenvalues and eigenfunctions, human pose estimation, HUMANS, Image motion analysis, IMAGE PROCESSING, image registration, Image segmentation, Image sequences, kinematic singularity, Laplacian eigenmaps, multicamera tracking algorithm, pixel displacement, pose estimation, single-frame registration technique, temporal registration method, tracking},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2009.2022290},
	author = {Sundaresan, A. and Chellapa, Rama}
}
@conference {15064,
	title = {An improved mean shift tracking method based on nonparametric clustering and adaptive bandwidth},
	booktitle = {Machine Learning and Cybernetics, 2008 International Conference on},
	volume = {5},
	year = {2008},
	month = {2008/07//},
	pages = {2779 - 2784},
	abstract = {An improved mean shift method for object tracking based on nonparametric clustering and adaptive bandwidth is presented in this paper. Based on partitioning the color space of a tracked object by using a modified nonparametric clustering, an appearance model of the tracked object is built. It captures both the color information and spatial layout of the tracked object. The similarity measure between the target model and the target candidate is derived from the Bhattacharyya coefficient. The kernel bandwidth parameters are automatically selected by maximizing the lower bound of a log-likelihood function, which is derived from a kernel density estimate using the bandwidth matrix and the modified weight function. The experimental results show that the method can converge in an average of 2.6 iterations per frame.},
	keywords = {adaptive bandwidth, appearance model, bandwidth matrix, Bhattacharyya coefficient, color information, color space partitioning, image colour analysis, iterative procedure, kernel bandwidth parameter, kernel density estimate, log-likelihood function, mean shift tracking method, modified weight function, nonparametric clustering, Object detection, object representation, object tracking, pattern clustering, similarity measure, spatial layout, target candidate, target model, tracking},
	doi = {10.1109/ICMLC.2008.4620880},
	author = {Zhuolin Jiang and Li,Shao-Fa and Jia,Xi-Ping and Zhu,Hong-Li}
}
@conference {12582,
	title = {In Situ Evaluation of Tracking Algorithms Using Time Reversed Chains},
	booktitle = {Computer Vision and Pattern Recognition, 2007. CVPR {\textquoteright}07. IEEE Conference on},
	year = {2007},
	month = {2007/06//},
	pages = {1 - 8},
	abstract = {Automatic evaluation of visual tracking algorithms in the absence of ground truth is a very challenging and important problem. In the context of online appearance modeling, there is an additional ambiguity involving the correctness of the appearance model. In this paper, we propose a novel performance evaluation strategy for tracking systems based on particle filter using a time reversed Markov chain. Starting from the latest observation, the time reversed chain is propagated back till the starting time t = 0 of the tracking algorithm. The posterior density of the time reversed chain is also computed. The distance between the posterior density of the time reversed chain (at t = 0) and the prior density used to initialize the tracking algorithm forms the decision statistic for evaluation. It is postulated that when the data is generated true to the underlying models, the decision statistic takes a low value. We empirically demonstrate the performance of the algorithm against various common failure modes in the generic visual tracking problem. Finally, we derive a small frame approximation that allows for very efficient computation of the decision statistic.},
	keywords = {(numerical, algorithm;Markov, chain;tracking, decision, density;time, detection;particle, Evaluation, evaluation;object, filter;performance, Filtering, Markov, methods);tracking;visual, processes;decision, reversed, servoing;, situ, statistics;in, strategy;posterior, systems;visual, theory;object, tracking, tracking;particle},
	doi = {10.1109/CVPR.2007.382992},
	author = {Wu,Hao and Sankaranarayanan,A. C and Chellapa, Rama}
}
@conference {13143,
	title = {Object Tracking at Multiple Levels of Spatial Resolutions},
	booktitle = {Image Analysis and Processing, 2007. ICIAP 2007. 14th International Conference on},
	year = {2007},
	month = {2007/09//},
	pages = {149 - 154},
	abstract = {Tracking is usually performed at a single level of data resolution. This paper describes a multi-resolution tracking framework developed with efficiency and robustness in mind. Efficiency is achieved by processing low resolution data whenever possible. Robustness results from multiple level coarse-to-fine searching in the tracking state space. We combine sequential filtering both in time and resolution levels into a probabilistic framework. A color blob tracker is implemented and the tracking results are evaluated in a number of experiments.},
	keywords = {coarse-to-fine, detection;tracking;, filtering;spatial, framework;object, levels;tracking, multiple, resolution;multiresolution, resolutions, searching;data, space;computer, state, tracking, tracking;sequential, vision;object},
	doi = {10.1109/ICIAP.2007.4362772},
	author = {Tran,S.D. and Davis, Larry S.}
}
@conference {13137,
	title = {Robust Object Tracking with Regional Affine Invariant Features},
	booktitle = {Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on},
	year = {2007},
	month = {2007/10//},
	pages = {1 - 8},
	abstract = {We present a tracking algorithm based on motion analysis of regional affine invariant image features. The tracked object is represented with a probabilistic occupancy map. Using this map as support, regional features are detected and probabilistically matched across frames. The motion of pixels is then established based on the feature motion. The object occupancy map is in turn updated according to the pixel motion consistency. We describe experiments to measure the sensitivities of our approach to inaccuracy in initialization, and compare it with other approaches.},
	keywords = {affine, algorithm;feature, analysis;image, analysis;pixel, consistency;regional, detection;motion, detection;tracking;, extraction;image, feature, features;robust, invariant, matching;image, MOTION, object, resolution;object, tracking},
	doi = {10.1109/ICCV.2007.4408948},
	author = {Tran,Son and Davis, Larry S.}
}
@inbook {12640,
	title = {Computer Vision, Statistics in},
	booktitle = {Encyclopedia of Statistical SciencesEncyclopedia of Statistical Sciences},
	year = {2006},
	month = {2006///},
	publisher = {John Wiley \& Sons, Inc.},
	organization = {John Wiley \& Sons, Inc.},
	abstract = {Computer Vision (CV) broadly refers to the discipline where extraction of useful 2D and/or 3D information from one or more images is of interest. Useful information could consist of features such as edges, lines, curves and textures, or information about depth, motion, object descriptions, etc. CV problems are usually ill-posed inverse problems. Since the image data is usually obtained from sensors such as video cameras, infrared, radar, etc., the information extraction processes often have to deal with data that is corrupted by noise from the sensors and the environment. Statistics can help in obtaining robust and accurate solutions to these inverse problems by modeling the noise processes. We present here a broad overview of the applications of statistics to different computer vision problems and explain in detail two particular applications, tracking and motion analysis, where statistical approaches have been used very successfully.},
	keywords = {Computer vision, sampling, Statistics, structure from motion, tracking},
	isbn = {9780471667193},
	url = {http://onlinelibrary.wiley.com/doi/10.1002/0471667196.ess3129/abstract},
	author = {Chellapa, Rama and Chowdhury,Amit K. Roy}
}
@article {19661,
	title = {How Multirobot Systems Research will Accelerate our Understanding of Social Animal Behavior},
	journal = {Proceedings of the IEEE},
	volume = {94},
	year = {2006},
	month = {2006/07//},
	pages = {1445 - 1463},
	abstract = {Our understanding of social insect behavior has significantly influenced artificial intelligence (AI) and multirobot systems{\textquoteright} research (e.g., ant algorithms and swarm robotics). In this work, however, we focus on the opposite question: "How can multirobot systems research contribute to the understanding of social animal behavior?" As we show, we are able to contribute at several levels. First, using algorithms that originated in the robotics community, we can track animals under observation to provide essential quantitative data for animal behavior research. Second, by developing and applying algorithms originating in speech recognition and computer vision, we can automatically label the behavior of animals under observation. In some cases the automatic labeling is more accurate and consistent than manual behavior identification. Our ultimate goal, however, is to automatically create, from observation, executable models of behavior. An executable model is a control program for an agent that can run in simulation (or on a robot). The representation for these executable models is drawn from research in multirobot systems programming. In this paper we present the algorithms we have developed for tracking, recognizing, and learning models of social animal behavior, details of their implementation, and quantitative experimental results using them to study social insects},
	keywords = {Acceleration, Animal behavior, ant movement tracking, Artificial intelligence, biology computing, Computer vision, control engineering computing, Insects, Intelligent robots, Labeling, monkey movement tracking, multi-robot systems, multirobot systems, robotics algorithms, Robotics and automation, social animal behavior, social animals, social insect behavior, Speech recognition, tracking},
	isbn = {0018-9219},
	author = {Balch, T. and Dellaert, F. and Feldman, A. and Guillory, A. and Isbell, C.L. and Zia Khan and Pratt, S.C. and Stein, A.N. and Wilde, H.}
}
@conference {17854,
	title = {Model-based OpenMP implementation of a 3D facial pose tracking system},
	booktitle = {2006 International Conference on Parallel Processing Workshops, 2006. ICPP 2006 Workshops},
	year = {2006},
	month = {2006///},
	pages = {8 pp.-73 - 8 pp.-73},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Most image processing applications are characterized by computation-intensive operations, and high memory and performance requirements. Parallelized implementation on shared-memory systems offer an attractive solution to this class of applications. However, we cannot thoroughly exploit the advantages of such architectures without proper modeling and analysis of the application. In this paper, we describe our implementation of a 3D facial pose tracking system using the OpenMP platform. Our implementation is based on a design methodology that uses coarse-grain dataflow graphs to model and schedule the application. We present our modeling approach, details of the implementation that we derived based on this modeling approach, and associated performance results. The parallelized implementation achieves significant speedup, and meets or exceeds the target frame rate under various configurations},
	keywords = {3D facial pose tracking system, application modeling, application program interfaces, application scheduling, coarse-grain dataflow graphs, Concurrent computing, data flow graphs, Educational institutions, face recognition, IMAGE PROCESSING, image processing applications, Inference algorithms, Message passing, OpenMP platform, parallel implementation, PARALLEL PROCESSING, parallel programming, Particle tracking, Processor scheduling, SHAPE, shared memory systems, shared-memory systems, Solid modeling, tracking},
	isbn = {0-7695-2637-3},
	doi = {10.1109/ICPPW.2006.55},
	author = {Saha,S. and Chung-Ching Shen and Chia-Jui Hsu and Aggarwal,G. and Veeraraghavan,A. and Sussman, Alan and Bhattacharyya, Shuvra S.}
}
@conference {13173,
	title = {Top-down, bottom-up multivalued default reasoning for identity maintenance},
	booktitle = {Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks},
	series = {VSSN {\textquoteright}06},
	year = {2006},
	month = {2006///},
	pages = {79 - 86},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Persistent tracking systems require the capacity to track individuals by maintaining identity across visibility gaps caused by occlusion events. In traditional computer vision systems, the flow of information is typically bottom-up. The low level image processing modules take video input, perform early vision tasks such as background subtraction and object detection,and pass this information to the high level reasoning module. This paper describes the architecture of a system that uses top-down information flow to perform identity maintenance across occlusion events. This system uses the high level reasoning module to provide control feedback to the low level image processing module to perform forensic analysis of archival video and actively acquire information required to arrive at identity decisions. This functionality is in addition to traditional bottom-up reasoning about identity, employing contextual cues and appearance matching, within the multivalued default logic framework proposed in [18]. This framework, in addition to bestowing upon the system the property of nonmonotonicity, also allows for it to qualitatively encode its confidence in the identity decisions it takes.},
	keywords = {default logic, identity maintenance, nonmonotonic reasoning, tracking, visual surveillance},
	isbn = {1-59593-496-0},
	doi = {10.1145/1178782.1178795},
	url = {http://doi.acm.org/10.1145/1178782.1178795},
	author = {Shet,Vinay D. and Harwood,David and Davis, Larry S.}
}
@conference {12648,
	title = {Algorithmic and architectural design methodology for particle filters in hardware},
	booktitle = {Computer Design: VLSI in Computers and Processors, 2005. ICCD 2005. Proceedings. 2005 IEEE International Conference on},
	year = {2005},
	month = {2005/10//},
	pages = {275 - 280},
	abstract = {In this paper, we present algorithmic and architectural methodology for building particle filters in hardware. Particle filtering is a new paradigm for filtering in presence of nonGaussian nonlinear state evolution and observation models. This technique has found wide-spread application in tracking, navigation, detection problems especially in a sensing environment. So far most particle filtering implementations are not lucrative for real time problems due to excessive computational complexity involved. In this paper, we re-derive the particle filtering theory to make it more amenable to simplified VLSI implementations. Furthermore, we present and analyze pipelined architectural methodology for designing these computational blocks. Finally, we present an application using the bearing only tracking problem and evaluate the proposed architecture and algorithmic methodology.},
	keywords = {(numerical, algorithmic, architectural, architectures;, bearing, complexity;, computational, design, digital, evolution;, Filtering, filtering;, filters;, implementation;, methodology;, methods);, nonGaussian, nonlinear, only, Parallel, particle, pipeline, pipelined, problem;, processing;, state, tracking, VLSI, VLSI;},
	doi = {10.1109/ICCD.2005.20},
	author = {Sankaranarayanan,A. C and Chellapa, Rama and Srivastava, A.}
}
@conference {13196,
	title = {Efficient mean-shift tracking via a new similarity measure},
	booktitle = {Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on},
	volume = {1},
	year = {2005},
	month = {2005/06//},
	pages = {176 - 183 vol. 1 - 176 - 183 vol. 1},
	abstract = {The mean shift algorithm has achieved considerable success in object tracking due to its simplicity and robustness. It finds local minima of a similarity measure between the color histograms or kernel density estimates of the model and target image. The most typically used similarity measures are the Bhattacharyya coefficient or the Kullback-Leibler divergence. In practice, these approaches face three difficulties. First, the spatial information of the target is lost when the color histogram is employed, which precludes the application of more elaborate motion models. Second, the classical similarity measures are not very discriminative. Third, the sample-based classical similarity measures require a calculation that is quadratic in the number of samples, making real-time performance difficult. To deal with these difficulties we propose a new, simple-to-compute and more discriminative similarity measure in spatial-feature spaces. The new similarity measure allows the mean shift algorithm to track more general motion models in an integrated way. To reduce the complexity of the computation to linear order we employ the recently proposed improved fast Gauss transform. This leads to a very efficient and robust nonparametric spatial-feature tracking algorithm. The algorithm is tested on several image sequences and shown to achieve robust and reliable frame-rate tracking.},
	keywords = {algorithm;, analysis;, Bhattacharyya, coefficient;, Color, colour, density, divergence;, estimates;, extraction;, fast, feature, frame-rate, Gauss, Gaussian, histograms;, image, Kernel, Kullback-Leibler, matching;, Mean-shift, measures;, nonparametric, processes;, sample-based, sequences;, similarity, spaces;, spatial-feature, tracking, tracking;, transform;},
	doi = {10.1109/CVPR.2005.139},
	author = {Yang,Changjiang and Duraiswami, Ramani and Davis, Larry S.}
}
@conference {13186,
	title = {Fast multiple object tracking via a hierarchical particle filter},
	booktitle = {Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on},
	volume = {1},
	year = {2005},
	month = {2005/10//},
	pages = {212 - 219 Vol. 1 - 212 - 219 Vol. 1},
	abstract = {A very efficient and robust visual object tracking algorithm based on the particle filter is presented. The method characterizes the tracked objects using color and edge orientation histogram features. While the use of more features and samples can improve the robustness, the computational load required by the particle filter increases. To accelerate the algorithm while retaining robustness we adopt several enhancements in the algorithm. The first is the use of integral images for efficiently computing the color features and edge orientation histograms, which allows a large amount of particles and a better description of the targets. Next, the observation likelihood based on multiple features is computed in a coarse-to-fine manner, which allows the computation to quickly focus on the more promising regions. Quasi-random sampling of the particles allows the filter to achieve a higher convergence rate. The resulting tracking algorithm maintains multiple hypotheses and offers robustness against clutter or short period occlusions. Experimental results demonstrate the efficiency and effectiveness of the algorithm for single and multiple object tracking.},
	keywords = {(numerical, algorithm;, analysis;, Color, colour, Computer, Convergence, detection;, edge, fast, filter;, Filtering, hierarchical, histogram;, image, images;, integral, likelihood;, methods);, methods;, multiple, numerical, object, observation, of, orientation, particle, processes;, quasirandom, random, sampling;, tracking, tracking;, vision;, visual},
	doi = {10.1109/ICCV.2005.95},
	author = {Yang,Changjiang and Duraiswami, Ramani and Davis, Larry S.}
}
@conference {11908,
	title = {Pedestrian classification from moving platforms using cyclic motion pattern},
	booktitle = {IEEE International Conference on Image Processing, 2005. ICIP 2005},
	volume = {2},
	year = {2005},
	month = {2005/09//},
	pages = {II- 854-7 - II- 854-7},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {This paper describes an efficient pedestrian detection system for videos acquired from moving platforms. Given a detected and tracked object as a sequence of images within a bounding box, we describe the periodic signature of its motion pattern using a twin-pendulum model. Then a principle gait angle is extracted in every frame providing gait phase information. By estimating the periodicity from the phase data using a digital phase locked loop (dPLL), we quantify the cyclic pattern of the object, which helps us to continuously classify it as a pedestrian. Past approaches have used shape detectors applied to a single image or classifiers based on human body pixel oscillations, but ours is the first to integrate a global cyclic motion model and periodicity analysis. Novel contributions of this paper include: i) development of a compact shape representation of cyclic motion as a signature for a pedestrian, ii) estimation of gait period via a feedback loop module, and iii) implementation of a fast online pedestrian classification system which operates on videos acquired from moving platforms.},
	keywords = {compact shape representation, cyclic motion pattern, data mining, Detectors, digital phase locked loop, digital phase locked loops, feedback loop module, gait analysis, gait phase information, human body pixel oscillations, HUMANS, image classification, Image motion analysis, image representation, image sequence, Image sequences, Motion detection, Object detection, pedestrian classification, pedestrian detection system, Phase estimation, Phase locked loops, principle gait angle, SHAPE, tracking, Videos},
	isbn = {0-7803-9134-9},
	doi = {10.1109/ICIP.2005.1530190},
	author = {Yang Ran and Qinfen Zheng and Weiss, I. and Davis, Larry S. and Abd-Almageed, Wael and Liang Zhao}
}
@conference {12652,
	title = {Tracking objects in video using motion and appearance models},
	booktitle = {Image Processing, 2005. ICIP 2005. IEEE International Conference on},
	volume = {2},
	year = {2005},
	month = {2005/09//},
	pages = {II - 394-7 - II - 394-7},
	abstract = {This paper proposes a visual tracking algorithm that combines motion and appearance in a statistical framework. It is assumed that image observations are generated simultaneously from a background model and a target appearance model. This is different from conventional appearance-based tracking, that does not use motion information. The proposed algorithm attempts to maximize the likelihood ratio of the tracked region, derived from appearance and background models. Incorporation of motion in appearance based tracking provides robust tracking, even when the target violates the appearance model. We show that the proposed algorithm performs well in tracking targets efficiently over long time intervals.},
	keywords = {algorithm;, analysis;, appearance, background, estimation;, image, likelihood, maximum, model;, models;, MOTION, object, processing;, signal, target, tracking, tracking;, video, visual},
	doi = {10.1109/ICIP.2005.1530075},
	author = {Sankaranarayanan,A. C and Chellapa, Rama and Qinfen Zheng}
}
@conference {13214,
	title = {Object tracking by adaptive feature extraction},
	booktitle = {Image Processing, 2004. ICIP {\textquoteright}04. 2004 International Conference on},
	volume = {3},
	year = {2004},
	month = {2004/10//},
	pages = {1501 - 1504 Vol. 3 - 1501 - 1504 Vol. 3},
	abstract = {Tracking objects in the high-dimensional feature space is not only computationally expensive but also functionally inefficient. Selecting a low-dimensional discriminative feature set is a critical step to improve tracker performance. A good feature set for tracking can differ from frame to frame due to the changes in the background against the tracked object, and due to an on-line algorithm that adaptively determines a advantageous distinctive feature set. In this paper, multiple heterogeneous features are assembled, and likelihood images are constructed for various subspaces of the combined feature space. Then, the most discriminative feature is extracted by principal component analysis (PCA) based on those likelihood images. This idea is applied to the mean-shift tracking algorithm [D. Comaniciu et al., June 2000], and we demonstrate its effectiveness through various experiments.},
	keywords = {adaptive, algorithm;, analysis;, colour, component, extraction;, feature, feature;, heterogeneous, image, image;, likelihood, Mean-shift, object, online, principal, tracking, tracking;},
	doi = {10.1109/ICIP.2004.1421349},
	author = {Han,Bohyung and Davis, Larry S.}
}
@conference {12702,
	title = {Robust two-camera tracking using homography},
	booktitle = {Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP {\textquoteright}04). IEEE International Conference on},
	volume = {3},
	year = {2004},
	month = {2004/05//},
	pages = {iii - 1-4 vol.3 - iii - 1-4 vol.3},
	abstract = {The paper introduces a two view tracking method which uses the homography relation between the two views to handle occlusions. An adaptive appearance-based model is incorporated in a particle filter to realize robust visual tracking. Occlusion is detected using robust statistics. When there is occlusion in one view, the homography from this view to other views is estimated from previous tracking results and used to infer the correct transformation for the occluded view. Experimental results show the robustness of the two view tracker.},
	keywords = {Carlo, filter;, filters;, frame, framework;, homography;, image, method;, methods;, Monte, nonlinear, occlusions;, optical, particle, processing;, robust, sequences;, sequential, signal, statistics;, tracking, tracking;, two, two-camera, video, view, visual},
	doi = {10.1109/ICASSP.2004.1326466},
	author = {Yue,Zhanfeng and Zhou,S. K and Chellapa, Rama}
}
@conference {12684,
	title = {Simultaneous background and foreground modeling for tracking in surveillance video},
	booktitle = {Image Processing, 2004. ICIP {\textquoteright}04. 2004 International Conference on},
	volume = {2},
	year = {2004},
	month = {2004/10//},
	pages = {1053 - 1056 Vol.2 - 1053 - 1056 Vol.2},
	abstract = {We present a stochastic tracking algorithm for surveillance video where targets are dim and at low resolution. The algorithm builds motion models for both background and foreground by integrating motion and intensity information. Some other merits of the algorithm include adaptive selection of feature points for scene description and defining proper cost functions for displacement estimation. The experimental results show tracking robustness and precision in a challenging video sequences.},
	keywords = {algorithm;, analysis;, background-foreground, displacement, estimation;, image, information;, INTENSITY, modeling;, MOTION, processes;, processing;, resolution;, sequences;, signal, Stochastic, Surveillance, surveillance;, tracking, tracking;, video},
	doi = {10.1109/ICIP.2004.1419483},
	author = {Shao, J. and Zhou,S. K and Chellapa, Rama}
}
@article {12680,
	title = {Video-Based Automatic Target Recognition},
	year = {2004},
	month = {2004/12//},
	institution = {MARYLAND UNIV COLLEGE PARK CENTER FOR AUTOMATION RESEARCH},
	abstract = {We present an approach for vehicle classification in IR video sequences by integrating detection, tracking and recognition. The method has two steps. First, the moving target is automatically detected using a detection algorithm. Next, we perform simultaneous tracking and recognition using an appearance-model based particle filter. The tracking result is evaluated at each frame. Low confidence in tracking performance initiates a new cycle of detection, tracking and classification. We demonstrate the robustness of the proposed method using outdoor IR video sequences.},
	keywords = {*SEQUENCES(MATHEMATICS), *TARGET RECOGNITION, *VEHICLES, *VIDEO RECORDING, algorithms, AUTOMATIC TRACKING., COMPONENT REPORTS, MOVING TARGETS, SURFACE TRANSPORTATION AND EQUIPMENT, SYMPOSIA, TARGET CLASSIFICATION, TARGET DIRECTION, RANGE AND POSITION FINDING, tracking, VIDEO SIGNALS},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA431617},
	author = {Zhou,S. K and Chellapa, Rama and Mei,Xue and Wu,Hao and Qinfen Zheng}
}
@article {12682,
	title = {Visual tracking and recognition using appearance-adaptive models in particle filters},
	journal = {IEEE Transactions on Image Processing},
	volume = {13},
	year = {2004},
	month = {2004/11//},
	pages = {1491 - 1506},
	abstract = {We present an approach that incorporates appearance-adaptive models in a particle filter to realize robust visual tracking and recognition algorithms. Tracking needs modeling interframe motion and appearance changes, whereas recognition needs modeling appearance changes between frames and gallery images. In conventional tracking algorithms, the appearance model is either fixed or rapidly changing, and the motion model is simply a random walk with fixed noise variance. Also, the number of particles is typically fixed. All these factors make the visual tracker unstable. To stabilize the tracker, we propose the following modifications: an observation model arising from an adaptive appearance model, an adaptive velocity motion model with adaptive noise variance, and an adaptive number of particles. The adaptive-velocity model is derived using a first-order linear predictor based on the appearance difference between the incoming observation and the previous particle configuration. Occlusion analysis is implemented using robust statistics. Experimental results on tracking visual objects in long outdoor and indoor video sequences demonstrate the effectiveness and robustness of our tracking algorithm. We then perform simultaneous tracking and recognition by embedding them in a particle filter. For recognition purposes, we model the appearance changes between frames and gallery images by constructing the intra- and extrapersonal spaces. Accurate recognition is achieved when confronted by pose and view variations.},
	keywords = {adaptive filters, adaptive noise variance, algorithms, appearance-adaptive model, Artificial intelligence, Cluster Analysis, Computer Graphics, Computer simulation, Feedback, Filtering, first-order linear predictor, hidden feature removal, HUMANS, Image Enhancement, Image Interpretation, Computer-Assisted, image recognition, Information Storage and Retrieval, Kinematics, Laboratories, Male, Models, Biological, Models, Statistical, MOTION, Movement, Noise robustness, Numerical Analysis, Computer-Assisted, occlusion analysis, Particle filters, Particle tracking, Pattern Recognition, Automated, Predictive models, Reproducibility of results, robust statistics, Sensitivity and Specificity, Signal Processing, Computer-Assisted, State estimation, statistical analysis, Subtraction Technique, tracking, Training data, visual recognition, visual tracking},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2004.836152},
	author = {Zhou,Shaohua Kevin and Chellapa, Rama and Moghaddam, B.}
}
@conference {11901,
	title = {A non-intrusive Kalman filter-based tracker for pursuit eye movement},
	booktitle = {American Control Conference, 2002. Proceedings of the 2002},
	volume = {2},
	year = {2002},
	month = {2002///},
	pages = {1443- 1447 vol.2 - 1443- 1447 vol.2},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {In this paper, we introduce a new non-intrusive approach to estimating the eye position during pursuit motion of the eye. We introduce a new characterization for the pursuit eye movement. Our characterization is based on the decomposition of the pursuit eye motion into a deterministic component and random component. We use a discrete Kalman filter to estimate the random component and calculate the deterministic component. We add the two components to obtain an estimate of the eye position. Simulation results are provided to illustrate the eye position estimation.},
	keywords = {Application software, characterization, Computer vision, Current measurement, deterministic component, Electric variables measurement, eye position estimation, eye tracking, gaze tracking, Human computer interaction, Kalman filter, Kalman filters, Lenses, Motion estimation, Optical reflection, pursuit eye movement, pursuit motion, random component, Skin, tracking},
	isbn = {0-7803-7298-0},
	doi = {10.1109/ACC.2002.1023224},
	url = {http://ieeexplore.ieee.org/ielx5/7965/22015/01023224.pdf?tp=\&arnumber=1023224\&isnumber=22015},
	author = {Abd-Almageed, Wael and Fadali,M. S and Bebis,G.}
}
@conference {18311,
	title = {Estimation of composite object and camera image motion},
	booktitle = {The Proceedings of the Seventh IEEE International Conference on Computer Vision, 1999},
	volume = {1},
	year = {1999},
	month = {1999///},
	pages = {190-197 vol.1 - 190-197 vol.1},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {An approach for estimating composite independent object and camera image motions is proposed. The approach employs spatio-temporal flow models learned through observing typical movements of the object, to decompose image motion into independent object and camera motions. The spatio-temporal flow models of the object motion are represented as a set of orthogonal flow bases that are learned using principal component analysis of instantaneous flow measurements from a stationary camera. These models are then employed in scenes with a moving camera to extract motion trajectories relative to those learned. The performance of the algorithm is demonstrated on several image sequences of rigid and articulated bodies in motion},
	keywords = {articulated bodies, camera image motion, camera image motions, CAMERAS, composite independent object, composite object estimation, Computer vision, Educational institutions, Image sequences, instantaneous flow measurements, Laboratories, Layout, Motion analysis, Motion estimation, motion trajectories, orthogonal flow bases, Principal component analysis, spatio-temporal flow models, tracking, Vehicles},
	isbn = {0-7695-0164-8},
	doi = {10.1109/ICCV.1999.791217},
	author = {Yacoob,Yaser and Davis, Larry S.}
}
@conference {18323,
	title = {Tracking rigid motion using a compact-structure constraint},
	booktitle = {Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on},
	volume = {1},
	year = {1999},
	month = {1999///},
	pages = {198 -205 vol.1 - 198 -205 vol.1},
	abstract = {An approach for tracking the motion of a rigid object using parameterized flow models and a compact-structure constraint is proposed. While polynomial parameterized flow models have been shown to be effective in tracking the rigid motion of planar objects, these models are inappropriate for tracking moving objects that change appearance revealing their 3D structure. We extend these models by adding a structure-compactness constraint that accounts for image motion that deviates from a planar structure. The constraint is based on the assumption that object structure variations are limited with respect to planar object projection onto the image plane and therefore can be expressed as a direct constraint on the image motion. The performance of the algorithm is demonstrated on several long image sequences of rigidly moving objects},
	keywords = {compact-structure constraint, image motion, Image sequences, Motion estimation, parameterized flow models, polynomial parameterized flow models, Polynomials, rigid motion tracking, rigid object, rigidly moving objects, structure-compactness constraint, tracking},
	doi = {10.1109/ICCV.1999.791218},
	author = {Yacoob,Yaser and Davis, Larry S.}
}
@conference {18322,
	title = {Computing 3-D head orientation from a monocular image sequence},
	booktitle = {Automatic Face and Gesture Recognition, 1996., Proceedings of the Second International Conference on},
	year = {1996},
	month = {1996/10//},
	pages = {242 - 247},
	abstract = {An approach for estimating 3D head orientation in a monocular image sequence is proposed. The approach employs recently developed image-based parameterized tracking for face and face features to locate the area in which a sub-pixel parameterized shape estimation of the eye{\textquoteright}s boundary is performed. This involves tracking of five points (four at the eye corners and the fifth is the lip of the nose). The authors describe an approach that relies on the coarse structure of the face to compute orientation relative to the camera plane. Our approach employs projective invariance of the cross-ratios of the eye corners and anthropometric statistics to estimate the head yaw, roll and pitch. Analytical and experimental results are reported},
	keywords = {3D head orientation computation, anthropometric statistics, camera plane, coarse structure, eye, eye boundary, eye corners, face features, face recognition, Feature extraction, head pitch, head roll, head yaw, Image sequences, image-based parameterized tracking, monocular image sequence, projective cross-ratio invariance, sub-pixel parameterized shape estimation, tracking},
	doi = {10.1109/AFGR.1996.557271},
	author = {Horprasert,T. and Yacoob,Yaser and Davis, Larry S.}
}
@article {18317,
	title = {Recognizing human facial expressions from long image sequences using optical flow},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {18},
	year = {1996},
	month = {1996/06//},
	pages = {636 - 642},
	abstract = {An approach to the analysis and representation of facial dynamics for recognition of facial expressions from image sequences is presented. The algorithms utilize optical flow computation to identify the direction of rigid and nonrigid motions that are caused by human facial expressions. A mid-level symbolic representation motivated by psychological considerations is developed. Recognition of six facial expressions, as well as eye blinking, is demonstrated on a large set of image sequences},
	keywords = {Computer vision, Eyebrows, face recognition, facial dynamics, Facial features, human facial expression recognition, HUMANS, Image motion analysis, image recognition, image representation, Image sequences, Motion analysis, Motion estimation, Optical computing, optical flow, symbolic representation, tracking},
	isbn = {0162-8828},
	doi = {10.1109/34.506414},
	author = {Yacoob,Yaser and Davis, Larry S.}
}
@conference {14246,
	title = {Perceptual computational advantages of tracking},
	booktitle = {, 11th IAPR International Conference on Pattern Recognition, 1992. Vol.I. Conference A: Computer Vision and Applications, Proceedings},
	year = {1992},
	month = {1992/09/30/Aug-3},
	pages = {599 - 602},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The paradigm of active vision advocates studying visual problems in the form of modules that are directly related to a visual task for observers that are active. It is argued that in many cases when an object is moving in an unrestricted manner (translation and rotation) in the 3D world only the motion{\textquoteright}s translational components are of interest. For a monocular observer, using only the normal flow-the spatiotemporal derivatives of the image intensity function-the authors solve the problem of computing the direction of translation. Their strategy uses fixation and tracking. Fixation simplifies much of the computation by placing the object at the center of the visual field, and the main advantage of tracking is the accumulation of information over time. The authors show how tracking is accomplished using normal flow measurements and use it for two different tasks in the solution process. First, it serves as a tool to compensate for the lack of existence of an optical flow field and thus to estimate the translation parallel to the image plane; and second, it gathers information about the motion component perpendicular to the image plane},
	keywords = {active vision, Automation, Employment, fixation, image intensity function, Image motion analysis, IMAGE PROCESSING, Motion estimation, Nonlinear optics, Optical computing, Optical sensors, parameter estimation, pattern recognition, perceptual computational advantages, spatiotemporal derivatives, Spatiotemporal phenomena, tracking, unrestricted motion, visual flow measurements},
	isbn = {0-8186-2910-X},
	doi = {10.1109/ICPR.1992.201633},
	author = {Ferm{\"u}ller, Cornelia and Aloimonos, J.}
}
