@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 {12464,
	title = {Video Pr{\'e}cis: Highlighting Diverse Aspects of Videos},
	journal = {IEEE Transactions on Multimedia},
	volume = {12},
	year = {2010},
	month = {2010/12//},
	pages = {853 - 868},
	abstract = {Summarizing long unconstrained videos is gaining importance in surveillance, web-based video browsing, and video-archival applications. Summarizing a video requires one to identify key aspects that contain the essence of the video. In this paper, we propose an approach that optimizes two criteria that a video summary should embody. The first criterion, {\textquotedblleft}coverage,{\textquotedblright} requires that the summary be able to represent the original video well. The second criterion, {\textquotedblleft}diversity,{\textquotedblright} requires that the elements of the summary be as distinct from each other as possible. Given a user-specified summary length, we propose a cost function to measure the quality of a summary. The problem of generating a précis is then reduced to a combinatorial optimization problem of minimizing the proposed cost function. We propose an efficient method to solve the optimization problem. We demonstrate through experiments (on KTH data, unconstrained skating video, a surveillance video, and a YouTube home video) that optimizing the proposed criterion results in meaningful video summaries over a wide range of scenarios. Summaries thus generated are then evaluated using both quantitative measures and user studies.},
	keywords = {$K$-means, CAMERAS, combinatorial mathematics, combinatorial optimization, Cost function, data compression, Exemplar selection, Image segmentation, Internet, Iron, Length measurement, multimedia systems, Ncut, optimisation, Optimization methods, original video, Permission, shot segmentation, Surveillance, user specified summary length, video précis, Video sharing, video signal processing, Video summarization},
	isbn = {1520-9210},
	doi = {10.1109/TMM.2010.2058795},
	author = {Shroff, N. and Turaga,P. and Chellapa, Rama}
}
@conference {13108,
	title = {Assigning cameras to subjects in video surveillance systems},
	booktitle = {Robotics and Automation, 2009. ICRA {\textquoteright}09. IEEE International Conference on},
	year = {2009},
	month = {2009/05//},
	pages = {837 - 843},
	abstract = {We consider the problem of tracking multiple agents moving amongst obstacles, using multiple cameras. Given an environment with obstacles, and many people moving through it, we construct a separate narrow field of view video for as many people as possible, by stitching together video segments from multiple cameras over time. We employ a novel approach to assign cameras to people as a function of time, with camera switches when needed. The problem is modeled as a bipartite graph and the solution corresponds to a maximum matching. As people move, the solution is efficiently updated by computing an augmenting path rather than by solving for a new matching. This reduces computation time by an order of magnitude. In addition, solving for the shortest augmenting path minimizes the number of camera switches at each update. When not all people can be covered by the available cameras, we cluster as many people as possible into small groups, then assign cameras to groups using a minimum cost matching algorithm. We test our method using numerous runs from different simulators.},
	keywords = {agent, algorithm;multiple, assignment;computation, augmenting, cameras;video, cost, detection;video, graph;camera, MATCHING, matching;minimum, matching;target, path;bipartite, reduction;maximum, segment;video, Surveillance, surveillance;, system;graph, theory;image, TIME, tracking;obstacle, tracking;video, video},
	doi = {10.1109/ROBOT.2009.5152753},
	author = {El-Alfy,H. and Jacobs, David W. and Davis, Larry S.}
}
@inbook {12536,
	title = {Knowledge Extraction from Surveillance Sensors},
	booktitle = {Wiley Handbook of Science and Technology for Homeland SecurityWiley Handbook of Science and Technology for Homeland Security},
	year = {2009},
	month = {2009///},
	publisher = {John Wiley \& Sons, Inc.},
	organization = {John Wiley \& Sons, Inc.},
	keywords = {multi-camera tracking, multi-modal fusion, recognition, sensor networks, Surveillance},
	isbn = {9780470087923},
	url = {http://onlinelibrary.wiley.com/doi/10.1002/9780470087923.hhs510/abstract;jsessionid=D0D752EF5D04327BE003BDFBD2F96134.d01t03},
	author = {Chellapa, Rama and Veeraraghavan,Ashok and Sankaranarayanan,Aswin C.}
}
@article {12523,
	title = {Unsupervised view and rate invariant clustering of video sequences},
	journal = {Computer Vision and Image Understanding},
	volume = {113},
	year = {2009},
	month = {2009/03//},
	pages = {353 - 371},
	abstract = {Videos play an ever increasing role in our everyday lives with applications ranging from news, entertainment, scientific research, security and surveillance. Coupled with the fact that cameras and storage media are becoming less expensive, it has resulted in people producing more video content than ever before. This necessitates the development of efficient indexing and retrieval algorithms for video data. Most state-of-the-art techniques index videos according to the global content in the scene such as color, texture, brightness, etc. In this paper, we discuss the problem of activity-based indexing of videos. To address the problem, first we describe activities as a cascade of dynamical systems which significantly enhances the expressive power of the model while retaining many of the computational advantages of using dynamical models. Second, we also derive methods to incorporate view and rate-invariance into these models so that similar actions are clustered together irrespective of the viewpoint or the rate of execution of the activity. We also derive algorithms to learn the model parameters from a video stream and demonstrate how a single video sequence may be clustered into different clusters where each cluster represents an activity. Experimental results for five different databases show that the clusters found by the algorithm correspond to semantically meaningful activities.},
	keywords = {Affine invariance, Cascade of linear dynamical systems, Rate invariance, Summarization, Surveillance, Video clustering, View invariance},
	isbn = {1077-3142},
	doi = {10.1016/j.cviu.2008.08.009},
	url = {http://www.sciencedirect.com/science/article/pii/S1077314208001367},
	author = {Turaga,Pavan and Veeraraghavan,Ashok and Chellapa, Rama}
}
@article {12549,
	title = {A Constrained Probabilistic Petri Net Framework for Human Activity Detection in Video},
	journal = {Multimedia, IEEE Transactions on},
	volume = {10},
	year = {2008},
	month = {2008/10//},
	pages = {982 - 996},
	abstract = {Recognition of human activities in restricted settings such as airports, parking lots and banks is of significant interest in security and automated surveillance systems. In such settings, data is usually in the form of surveillance videos with wide variation in quality and granularity. Interpretation and identification of human activities requires an activity model that a) is rich enough to handle complex multi-agent interactions, b) is robust to uncertainty in low-level processing and c) can handle ambiguities in the unfolding of activities. We present a computational framework for human activity representation based on Petri nets. We propose an extension-Probabilistic Petri Nets (PPN)-and show how this model is well suited to address each of the above requirements in a wide variety of settings. We then focus on answering two types of questions: (i) what are the minimal sub-videos in which a given activity is identified with a probability above a certain threshold and (ii) for a given video, which activity from a given set occurred with the highest probability? We provide the PPN-MPS algorithm for the first problem, as well as two different algorithms (naive PPN-MPA and PPN-MPA) to solve the second. Our experimental results on a dataset consisting of bank surveillance videos and an unconstrained TSA tarmac surveillance dataset show that our algorithms are both fast and provide high quality results.},
	keywords = {activity, dataset;automated, detection;human, interactions;security, net;human, nets;image, Petri, probabilistic, processing;multiagent, processing;video, representation;low-level, representation;video, signal, Surveillance, surveillance;, systems;constrained, systems;surveillance, tarmac, TSA, videos;Petri},
	isbn = {1520-9210},
	doi = {10.1109/TMM.2008.2001369},
	author = {Albanese, M. and Chellapa, Rama and Moscato, V. and Picariello, A. and V.S. Subrahmanian and Turaga,P. and Udrea,O.}
}
@conference {12543,
	title = {Learning action dictionaries from video},
	booktitle = {Image Processing, 2008. ICIP 2008. 15th IEEE International Conference on},
	year = {2008},
	month = {2008/10//},
	pages = {1704 - 1707},
	abstract = {Summarizing the contents of a video containing human activities is an important problem in computer vision and has important applications in automated surveillance systems. Summarizing a video requires one to identify and learn a {\textquoteright}vocabulary{\textquoteright} of action-phrases corresponding to specific events and actions occurring in the video. We propose a generative model for dynamic scenes containing human activities as a composition of independent action-phrases - each of which is derived from an underlying vocabulary. Given a long video sequence, we propose a completely unsupervised approach to learn the vocabulary. Once the vocabulary is learnt, a video segment can be decomposed into a collection of phrases for summarization. We then describe methods to learn the correlations between activities and sequentiality of events. We also propose a novel method for building invariances to spatial transforms in the summarization scheme.},
	keywords = {(artificial, action, action-phrases;learning, automated, decomposition;video, dictionaries;spatial, intelligence);video, segment, segmentation;learning, sequence;computer, Surveillance, surveillance;, systems;computer, transforms;video, vision;image, vision;independent},
	doi = {10.1109/ICIP.2008.4712102},
	author = {Turaga,P. and Chellapa, Rama}
}
@conference {15060,
	title = {A New Approach of Dynamic Background Modeling for Surveillance Information},
	booktitle = {Computer Science and Software Engineering, 2008 International Conference on},
	volume = {1},
	year = {2008},
	month = {2008/12//},
	pages = {850 - 855},
	abstract = {This paper presents a new approach of best background modeling for surveillance information. The approach makes orthogonal non-separable wavelet transformation of information frames used for background modeling, extracts the approximate information to reconstruct information frames, filters out the disturbance, shadow and noise from the reconstructed frames, constructs basic background with the method of binary mask images, uses multi-frame combination of non-uniform noise to filter noise in basic background, applies mutual information to detect the situation of adjacent changes. If the background has a gradual change, weighted superposition of multi background modeling images with time will be applied to update the background. If the background has a major or sudden change, the background will remodel from this frame.},
	keywords = {approximate information extraction, binary mask images, disturbance filtering, dynamic background modeling, Feature extraction, filtering theory, Image reconstruction, information frame reconstruction, NOISE, noise filtering, orthogonal nonseparable wavelet transformation, Surveillance, surveillance information, Wavelet transforms},
	doi = {10.1109/CSSE.2008.601},
	author = {Gao,Dongfa and Zhuolin Jiang and Ye,Ming}
}
@conference {13146,
	title = {Bilattice-based Logical Reasoning for Human Detection},
	booktitle = {Computer Vision and Pattern Recognition, 2007. CVPR {\textquoteright}07. IEEE Conference on},
	year = {2007},
	month = {2007/06//},
	pages = {1 - 8},
	abstract = {The capacity to robustly detect humans in video is a critical component of automated visual surveillance systems. This paper describes a bilattice based logical reasoning approach that exploits contextual information and knowledge about interactions between humans, and augments it with the output of different low level detectors for human detection. Detections from low level parts-based detectors are treated as logical facts and used to reason explicitly about the presence or absence of humans in the scene. Positive and negative information from different sources, as well as uncertainties from detections and logical rules, are integrated within the bilattice framework. This approach also generates proofs or justifications for each hypothesis it proposes. These justifications (or lack thereof) are further employed by the system to explain and validate, or reject potential hypotheses. This allows the system to explicitly reason about complex interactions between humans and handle occlusions. These proofs are also available to the end user as an explanation of why the system thinks a particular hypothesis is actually a human. We employ a boosted cascade of gradient histograms based detector to detect individual body parts. We have applied this framework to analyze the presence of humans in static images from different datasets.},
	keywords = {automated, detection;parts-based, detectors;static, histograms;human, images;formal, interactions;gradient, logic;image, logical, mechanisms;surveillance;video, processing;, reasoning;complex, recognition;inference, signal, Surveillance, systems;bilattice-based, visual},
	doi = {10.1109/CVPR.2007.383133},
	author = {Shet,V. D and Neumann, J. and Ramesh,V. and Davis, Larry S.}
}
@conference {12593,
	title = {Coarse-to-Fine Event Model for Human Activities},
	booktitle = {Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on},
	volume = {1},
	year = {2007},
	month = {2007/04//},
	pages = {I-813 -I-816 - I-813 -I-816},
	abstract = {We analyze coarse-to-fine hierarchical representation of human activities in video sequences. It can be used for efficient video browsing and activity recognition. Activities are modeled using a sequence of instantaneous events. Events in activities can be represented in a coarse-to-fine hierarchy in several ways, i.e., there may not be a unique hierarchical structure. We present five criteria and quantitative measures for evaluating their effectiveness. The criteria are minimalism, stability, consistency, accessibility and applicability. It is desirable to develop activity models that rank highly on these criteria at all levels of hierarchy. In this paper, activities are represented as sequence of event probabilities computed using the hidden Markov model framework. Two aspects of hierarchies are analyzed: the effect of reduced frame rate on the accuracy of events detected at a finer scale; and the effect of reduced spatial resolution on activity recognition. Experiments using the UCF indoor human action dataset and the TSA airport tarmac surveillance dataset show encouraging results},
	keywords = {action, activities;spatial, airport, browsing;video, dataset;activity, dataset;UCF, event, framework;human, human, indoor, Markov, model, model;event, models;image, probabilities;hidden, processing;, recognition;coarse-to-fine, reduction;video, representation;image, resolution, resolution;image, sequences;hidden, sequences;stability;video, signal, Surveillance, tarmac, TSA},
	doi = {10.1109/ICASSP.2007.366032},
	author = {Cuntoor, N.P. and Chellapa, Rama}
}
@conference {12584,
	title = {Epitomic Representation of Human Activities},
	booktitle = {Computer Vision and Pattern Recognition, 2007. CVPR {\textquoteright}07. IEEE Conference on},
	year = {2007},
	month = {2007/06//},
	pages = {1 - 8},
	abstract = {We introduce an epitomic representation for modeling human activities in video sequences. A video sequence is divided into segments within which the dynamics of objects is assumed to be linear and modeled using linear dynamical systems. The tuple consisting of the estimated system matrix, statistics of the input signal and the initial state value is said to form an epitome. The system matrices are decomposed using the Iwasawa matrix decomposition to isolate the effect of rotation, scaling and projective action on the state vector. "We demonstrate the usefulness of the proposed representation and decomposition for activity recognition using the TSA airport surveillance dataset and the UCF indoor human action dataset.},
	keywords = {action, activities, airport, dataset;epitomic, dataset;UCF, decomposition;modelling;statistics;video, decomposition;TSA, dynamical, human, indoor, Iwasawa, matrix, matrix;human, modeling;input, processing;, representation;estimated, sequences;image, sequences;matrix, signal, statistics;linear, Surveillance, system, systems;video},
	doi = {10.1109/CVPR.2007.383135},
	author = {Cuntoor, N.P. and Chellapa, Rama}
}
@conference {13142,
	title = {Learning Higher-order Transition Models in Medium-scale Camera Networks},
	booktitle = {Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on},
	year = {2007},
	month = {2007/10//},
	pages = {1 - 8},
	abstract = {We present a Bayesian framework for learning higher- order transition models in video surveillance networks. Such higher-order models describe object movement between cameras in the network and have a greater predictive power for multi-camera tracking than camera adjacency alone. These models also provide inherent resilience to camera failure, filling in gaps left by single or even multiple non-adjacent camera failures. Our approach to estimating higher-order transition models relies on the accurate assignment of camera observations to the underlying trajectories of objects moving through the network. We addresses this data association problem by gathering the observations and evaluating alternative partitions of the observation set into individual object trajectories. Searching the complete partition space is intractable, so an incremental approach is taken, iteratively adding observations and pruning unlikely partitions. Partition likelihood is determined by the evaluation of a probabilistic graphical model. When the algorithm has considered all observations, the most likely (MAP) partition is taken as the true object trajectories. From these recovered trajectories, the higher-order statistics we seek can be derived and employed for tracking. The partitioning algorithm we present is parallel in nature and can be readily extended to distributed computation in medium-scale smart camera networks.},
	keywords = {(artificial, approach;medium-scale, association, Bayesian, camera, cameras;video, framework;data, fusion;iterative, graphical, intelligence);optical, learning;incremental, likelihood;multicamera, likely, methods;higher, methods;learning, model, model;video, movement;probabilistic, network;Bayes, network;most, order, partition, problem;higher-order, statistics;higher-order, statistics;image, Surveillance, surveillance;, tracking;object, tracking;probability;video, transition},
	doi = {10.1109/ICCV.2007.4409203},
	author = {Farrell,R. and David Doermann and Davis, Larry S.}
}
@conference {13157,
	title = {Multi-scale video cropping},
	booktitle = {Proceedings of the 15th international conference on Multimedia},
	series = {MULTIMEDIA {\textquoteright}07},
	year = {2007},
	month = {2007///},
	pages = {97 - 106},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {We consider the problem of cropping surveillance videos. This process chooses a trajectory that a small sub-window can take through the video, selecting the most important parts of the video for display on a smaller monitor. We model the information content of the video simply, by whether the image changes at each pixel. Then we show that we can find the globally optimal trajectory for a cropping window by using a shortest path algorithm. In practice, we can speed up this process without affecting the results, by stitching together trajectories computed over short intervals. This also reduces system latency. We then show that we can use a second shortest path formulation to find good cuts from one trajectory to another, improving coverage of interesting events in the video. We describe additional techniques to improve the quality and efficiency of the algorithm, and show results on surveillance videos.},
	keywords = {shortest path algorithm, Surveillance, video cropping},
	isbn = {978-1-59593-702-5},
	doi = {10.1145/1291233.1291255},
	url = {http://doi.acm.org/10.1145/1291233.1291255},
	author = {El-Alfy,Hazem and Jacobs, David W. and Davis, Larry S.}
}
@conference {13181,
	title = {Segmenting people in small groups},
	booktitle = {Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks},
	series = {VSSN {\textquoteright}06},
	year = {2006},
	month = {2006///},
	pages = {1 - 2},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {In this paper we review prior work on the problem of segmenting small groups of people into individuals in surveillance video.},
	keywords = {segmentation, Surveillance, video analysis},
	isbn = {1-59593-496-0},
	doi = {10.1145/1178782.1178783},
	url = {http://doi.acm.org/10.1145/1178782.1178783},
	author = {Davis, Larry S.}
}
@conference {13191,
	title = {VidMAP: video monitoring of activity with Prolog},
	booktitle = {Advanced Video and Signal Based Surveillance, 2005. AVSS 2005. IEEE Conference on},
	year = {2005},
	month = {2005/09//},
	pages = {224 - 229},
	abstract = {This paper describes the architecture of a visual surveillance system that combines real time computer vision algorithms with logic programming to represent and recognize activities involving interactions amongst people, packages and the environments through which they move. The low level computer vision algorithms log primitive events of interest as observed facts, while the higher level Prolog based reasoning engine uses these facts in conjunction with predefined rules to recognize various activities in the input video streams. The system is illustrated in action on a multi-camera surveillance scenario that includes both security and safety violations.},
	keywords = {activities, algorithms;, based, Computer, computerised, engine;, higher, image, level, Logic, monitoring;, multicamera, processing;, programming;, Prolog, PROLOG;, reasoning, recognition;, scenario;, signal, streaming;, streams;, Surveillance, surveillance;, system;, video, VISION, vision;, visual},
	doi = {10.1109/AVSS.2005.1577271},
	author = {Shet,V. D and Harwood,D. and Davis, Larry S.}
}
@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}
}
@conference {12705,
	title = {Vehicle detection and tracking using acoustic and video sensors},
	booktitle = {Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP {\textquoteright}04). IEEE International Conference on},
	volume = {3},
	year = {2004},
	month = {2004/05//},
	pages = {iii - 793-6 vol.3 - iii - 793-6 vol.3},
	abstract = {Multimodal sensing has attracted much attention in solving a wide range of problems, including target detection, tracking, classification, activity understanding, speech recognition, etc. In surveillance applications, different types of sensors, such as video and acoustic sensors, provide distinct observations of ongoing activities. We present a fusion framework using both video and acoustic sensors for vehicle detection and tracking. In the detection phase, a rough estimate of target direction-of-arrival (DOA) is first obtained using acoustic data through beam-forming techniques. This initial DOA estimate designates the approximate target location in video. Given the initial target position, the DOA is refined by moving target detection using the video data. Markov chain Monte Carlo techniques are then used for joint audio-visual tracking. A novel fusion approach has been proposed for tracking, based on different characteristics of audio and visual trackers. Experimental results using both synthetic and real data are presented. Improved tracking performance has been observed by fusing the empirical posterior probability density functions obtained using both types of sensors.},
	keywords = {acoustic, applications;, audio, audio-visual, beam-forming, Carlo, chain, density, detection;, direction-of-arrival, DOA, empirical, estimation;, framework;, functions;, fusion, fusion;, joint, Markov, methods;, Monte, moving, multimodal, object, optical, posterior, probability, probability;, processes;, processing;, sensing;, sensor, sensors;, signal, Surveillance, surveillance;, systems;, target, techniques;, tracking;, vehicle, video},
	doi = {10.1109/ICASSP.2004.1326664},
	author = {Chellapa, Rama and Qian,Gang and Qinfen Zheng}
}
@conference {12729,
	title = {Activity recognition using the dynamics of the configuration of interacting objects},
	booktitle = {Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on},
	volume = {2},
	year = {2003},
	month = {2003/06//},
	pages = {II - 633-40 vol.2 - II - 633-40 vol.2},
	abstract = {Monitoring activities using video data is an important surveillance problem. A special scenario is to learn the pattern of normal activities and detect abnormal events from a very low resolution video where the moving objects are small enough to be modeled as point objects in a 2D plane. Instead of tracking each point separately, we propose to model an activity by the polygonal {\textquoteright}shape{\textquoteright} of the configuration of these point masses at any time t, and its deformation over time. We learn the mean shape and the dynamics of the shape change using hand-picked location data (no observation noise) and define an abnormality detection statistic for the simple case of a test sequence with negligible observation noise. For the more practical case where observation (point locations) noise is large and cannot be ignored, we use a particle filter to estimate the probability distribution of the shape given the noisy observations up to the current time. Abnormality detection in this case is formulated as a change detection problem. We propose a detection strategy that can detect both {\textquoteright}drastic{\textquoteright} and {\textquoteright}slow{\textquoteright} abnormalities. Our framework can be directly applied for object location data obtained using any type of sensors - visible, radar, infrared or acoustic.},
	keywords = {2D, abnormal, abnormality, abnormality;, acoustic, activity, analysis;, change;, Computer, configuration, configuration;, data;, DETECTION, detection;, distribution;, drastic, dynamics;, event;, filter;, hand-picked, image, infrared, interacting, learning;, location, low, mean, model;, monitoring;, MOTION, moving, noise;, noisy, object, object;, observation, observation;, particle, pattern, plane;, point, polygonal, probability, probability;, problem;, processing;, radar, recognition;, resolution, sensor;, sensors;, sequence;, SHAPE, shape;, signal, slow, statistic;, strategy;, Surveillance, surveillance;, target, test, tracking;, video, video;, visible, vision;},
	doi = {10.1109/CVPR.2003.1211526},
	author = {Vaswani, N. and RoyChowdhury, A. and Chellapa, Rama}
}
@conference {13244,
	title = {Image-based pan-tilt camera control in a multi-camera surveillance environment},
	booktitle = {Multimedia and Expo, 2003. ICME {\textquoteright}03. Proceedings. 2003 International Conference on},
	volume = {1},
	year = {2003},
	month = {2003/07//},
	pages = {I - 645-8 vol.1 - I - 645-8 vol.1},
	abstract = {In automated surveillance systems with multiple cameras, the system must be able to position the cameras accurately. Each camera must be able to pan-tilt such that an object detected in the scene is in a vantage position in the camera{\textquoteright}s image plane and subsequently capture images of that object. Typically, camera calibration is required. We propose an approach that uses only image-based information. Each camera is assigned a pan-tilt zero-position. Position of an object detected in one camera is related to the other cameras by homographies between the zero-positions while different pan-tilt positions of the same camera are related in the form of projective rotations. We then derive that the trajectories in the image plane corresponding to these projective rotations are approximately circular for pan and linear for tilt. The camera control technique is subsequently tested in a working prototype.},
	keywords = {automated, camera, cameras;, control;, detection;, environment;, image, image-based, information;, multicamera, object, pan-tilt, position;, processing;, sensors;, Surveillance, surveillance;, systems;, vantage, zero-position;, zero-positions;},
	doi = {10.1109/ICME.2003.1221000},
	author = {Lim,Ser-Nam and Elgammal,A. and Davis, Larry S.}
}
@conference {13245,
	title = {Scalable image-based multi-camera visual surveillance system},
	booktitle = {Proceedings. IEEE Conference on Advanced Video and Signal Based Surveillance, 2003.},
	year = {2003},
	month = {2003/07//},
	pages = {205 - 212},
	abstract = {We describe the design of a scalable and wide coverage visual surveillance system. Scalability (the ability to add and remove cameras easily during system operation with minimal overhead and system degradation) is achieved by utilizing only image-based information for camera control. We show that when a pan-tilt-zoom camera pans and tilts, a given image point moves in a circular and a linear trajectory, respectively. We create a scene model using a plan view of the scene. The scene model makes it easy for us to handle occlusion prediction and schedule video acquisition tasks subject to visibility constraints. We describe a maximum weight matching algorithm to assign cameras to tasks that meet the visibility constraints. The system is illustrated both through simulations and real video from a 6-camera configuration.},
	keywords = {ACQUISITION, algorithm;, camera;, constraints;, feature, hidden, image-based, MATCHING, maximum, multi-camera, occlusion, pan-tilt-zoom, PLAN, prediction;, processing;, removal;, scalable, scheduling;, signal, Surveillance, surveillance;, system;, task, video, view;, visibility, visual, weight},
	doi = {10.1109/AVSS.2003.1217923},
	author = {Lim,Ser-Nam and Davis, Larry S. and Elgammal,A.}
}
@conference {12733,
	title = {Statistical shape theory for activity modeling},
	booktitle = {Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP {\textquoteright}03). 2003 IEEE International Conference on},
	volume = {3},
	year = {2003},
	month = {2003/04//},
	pages = {III - 493-6 vol.3 - III - 493-6 vol.3},
	abstract = {Monitoring activities in a certain region from video data is an important surveillance problem. The goal is to learn the pattern of normal activities and detect unusual ones by identifying activities that deviate appreciably from the typical ones. We propose an approach using statistical shape theory based on the shape model of D.G. Kendall et al. (see "Shape and Shape Theory", John Wiley and Sons, 1999). In a low resolution video, each moving object is best represented as a moving point mass or particle. In this case, an activity can be defined by the interactions of all or some of these moving particles over time. We model this configuration of the particles by a polygonal shape formed from the locations of the points in a frame and the activity by the deformation of the polygons in time. These parameters are learned for each typical activity. Given a test video sequence, an activity is classified as abnormal if the probability for the sequence (represented by the mean shape and the dynamics of the deviations), given the model, is below a certain threshold The approach gives very encouraging results in surveillance applications using a single camera and is able to identify various kinds of abnormal behavior.},
	keywords = {abnormal, activities, activity, analysis;, behavior;, classification;, data;, image, mass;, matching;, modeling;, monitoring;, moving, normal, particle;, pattern, pattern;, point, polygonal, probability;, problem;, processing;, sequence;, sequences;, SHAPE, shape;, signal, statistical, Surveillance, surveillance;, theory;, video},
	doi = {10.1109/ICASSP.2003.1199519},
	author = {Vaswani, N. and Chowdhury, A.R. and Chellapa, Rama}
}
@conference {12735,
	title = {Video synthesis of arbitrary views for approximately planar scenes},
	booktitle = {Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP {\textquoteright}03). 2003 IEEE International Conference on},
	volume = {3},
	year = {2003},
	month = {2003/04//},
	pages = {III - 497-500 vol.3 - III - 497-500 vol.3},
	abstract = {In this paper, we propose a method to synthesize arbitrary views of a planar scene, given a monocular video sequence. The method is based on the availability of knowledge of the angle between the original and synthesized views. Such a method has many important applications, one of them being gait recognition. Gait recognition algorithms rely on the availability of an approximate side-view of the person. From a realistic viewpoint, such an assumption is impractical in surveillance applications and it is of interest to develop methods to synthesize a side view of the person, given an arbitrary view. For large distances from the camera, a planar approximation for the individual can be assumed. In this paper, we propose a perspective projection approach for recovering the direction of motion of the person purely from the video data, followed by synthesis of a new video sequence at a different angle. The algorithm works purely in the image and video domain, though 3D structure plays an implicit role in its theoretical justification. Examples of synthesized views using our method and performance evaluation are presented.},
	keywords = {(access, 3D, applications;, approach;, approximately, approximation;, arbitrary, Biometrics, control);, data;, direction, estimation;, evaluation;, Gait, image, monocular, MOTION, performance, perspective, planar, processing;, projection, recognition;, recovery;, scenes;, sequence;, sequences;, side, signal, structure;, Surveillance, surveillance;, synthesis;, synthesized, video, view, views;},
	doi = {10.1109/ICASSP.2003.1199520},
	author = {Chowdhury, A.K.R. and Kale, A. and Chellapa, Rama}
}
@conference {13583,
	title = {Live multimedia adaptation through wireless hybrid networks},
	booktitle = {Multimedia and Expo, 2000. ICME 2000. 2000 IEEE International Conference on},
	volume = {3},
	year = {2000},
	month = {2000///},
	pages = {1697 -1700 vol.3 - 1697 -1700 vol.3},
	abstract = {We present new techniques to intelligently adapt and combine multimedia presentations in real-time, mobile service environments. We present the techniques necessary to perform mobile multimedia processing for multiple video streams with ldquo;value-adding rdquo; information. The adaptation uses video analysis, content-recognition and automated annotation to provide scalable and interactive presentations over hybrid networks to mobile terminals. As a concrete case, we present a mobile surveillance service called Princess, which has several video sources and supports control information, terminal adaptation and end-to-end service},
	keywords = {adaptation;mobile, adaptation;value-adding, analysis;wireless, annotation;content-recognition;end-to-end, communication;multimedia, communication;real-time, hybrid, information;video, LAN;, multimedia, networks;mobile, Princess;automated, processing;mobile, service;live, service;mobile, streams;real-time, Surveillance, system;terminal, systems;surveillance;wireless, terminals;multiple, video},
	doi = {10.1109/ICME.2000.871098},
	author = {Koivisto,A. and Pietkainen,P. and Sauvola,J. and David Doermann}
}
@conference {11986,
	title = {Independent motion: the importance of history},
	booktitle = {Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on.},
	volume = {2},
	year = {1999},
	month = {1999///},
	pages = {97 Vol. 2 - 97 Vol. 2},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We consider a problem central in aerial visual surveillance applications-detection and tracking of small, independently moving objects in long and noisy video sequences. We directly use spatiotemporal image intensity gradient measurements to compute an exact model of background motion. This allows the creation of accurate mosaics over many frames and the definition of a constraint violation function which acts as an indication of independent motion. A novel temporal integration method maintains confidence measures over long subsequences without computing the optic flow, requiring object models, or using a Kalman filler. The mosaic acts as a stable feature frame, allowing precise localization of the independently moving objects. We present a statistical analysis of the effects of image noise on the constraint violation measure and find a good match between the predicted probability distribution function and the measured sample frequencies in a test sequence},
	keywords = {aerial visual surveillance, background image, Fluid flow measurement, Frequency measurement, History, Motion detection, Motion estimation, Motion measurement, Noise measurement, Optical computing, Optical noise, spatiotemporal image intensity gradient measurements, Spatiotemporal phenomena, Surveillance, Video sequences},
	isbn = {0-7695-0149-4},
	doi = {10.1109/CVPR.1999.784614},
	author = {Pless, R. and Brodsky, T. and Aloimonos, J.}
}
