@article {12474,
	title = {Applications of a Simple Characterization of Human Gait in Surveillance},
	journal = {Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on},
	volume = {40},
	year = {2010},
	month = {2010/08//},
	pages = {1009 - 1020},
	abstract = {Applications of a simple spatiotemporal characterization of human gait in the surveillance domain are presented. The approach is based on decomposing a video sequence into x-t slices, which generate periodic patterns referred to as double helical signatures (DHSs). The features of DHS are given as follows: 1) they naturally encode the appearance and kinematics of human motion and reveal geometric symmetries and 2) they are effective and efficient for recovering gait parameters and detecting simple events. We present an iterative local curve embedding algorithm to extract the DHS from video sequences. Two applications are then considered. First, the DHS is used for simultaneous segmentation and labeling of body parts in cluttered scenes. Experimental results showed that the algorithm is robust to size, viewing angles, camera motion, and severe occlusion. Then, the DHS is used to classify load-carrying conditions. By examining various symmetries in DHS, activities such as carrying, holding, and walking with objects that are attached to legs are detected. Our approach possesses several advantages: a compact representation that can be computed in real time is used; furthermore, it does not depend on silhouettes or landmark tracking, which are sensitive to errors in background subtraction stage.},
	keywords = {algorithms, Artificial intelligence, Automated;Photography;Reproducibility of Results;Sensitivity and Specificity;Video Recording;, Biometry, Computer-Assisted;Pattern Recognition, double helical signatures, Gait, gait analysis, human gait, human motion kinematics, HUMANS, Image Enhancement, Image Interpretation, Image motion analysis, iterative local curve embedding algorithm, Object detection, simple spatiotemporal characterization, video sequence, Video surveillance},
	isbn = {1083-4419},
	doi = {10.1109/TSMCB.2010.2044173},
	author = {Yang Ran and Qinfen Zheng and Chellapa, Rama and Strat, T.M.}
}
@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}
}
@conference {14205,
	title = {Bilateral symmetry of object silhouettes under perspective projection},
	booktitle = {19th International Conference on Pattern Recognition, 2008. ICPR 2008},
	year = {2008},
	month = {2008/12/08/11},
	pages = {1 - 4},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Symmetry is an important property of objects and is exhibited in different forms e.g., bilateral, rotational, etc. This paper presents an algorithm for computing the bilateral symmetry of silhouettes of shallow objects under perspective distortion, exploiting the invariance of the cross ratio to projective transformations. The basic idea is to use the cross ratio to compute a number of midpoints of cross sections and then fit a straight line through them. The goodness-of-fit determines the likelihood of the line to be the axis of symmetry. We analytically estimate the midpointpsilas location as a function of the vanishing point for a given object silhouette. Hence finding the symmetry axis amounts to a 2D search in the space of vanishing points. We present experiments on two datasets as well as Internet images of symmetric objects that validate our approach.},
	keywords = {Automation, bilateral symmetry, Computer vision, Frequency, Image analysis, Image coding, Image reconstruction, Internet, Internet images, Object detection, object silhouettes, perspective distortion, perspective projection, SHAPE, symmetric objects},
	isbn = {978-1-4244-2174-9},
	doi = {10.1109/ICPR.2008.4761501},
	author = {Bitsakos,K. and Yi,H. and Yi,L. and Ferm{\"u}ller, Cornelia}
}
@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 {15063,
	title = {An adaptive mean shift tracking method using multiscale images},
	booktitle = {Wavelet Analysis and Pattern Recognition, 2007. ICWAPR {\textquoteright}07. International Conference on},
	volume = {3},
	year = {2007},
	month = {2007/11//},
	pages = {1060 - 1066},
	abstract = {An adaptive mean shift tracking method for object tracking using multiscale images is presented in this paper. A bandwidth matrix and a Gaussian kernel are used to extend the definition of target model. The method can exactly estimate the position of the tracked object using multiscale images from Gaussian pyramid. The tracking method determines the parameters of kernel bandwidth by maximizing the lower bound of a log-likelihood function, which is derived from a kernel density estimate with the bandwidth matrix and the modified weight function. The experimental results show that it can averagely converge in 2.55 iterations per frame.},
	keywords = {adaptive mean shift tracking method, bandwidth matrix, Gaussian kernel, Gaussian processes, Image sequences, log-likelihood function, matrix algebra, maximum likelihood estimation, multiscale image, Object detection, object tracking},
	doi = {10.1109/ICWAPR.2007.4421589},
	author = {Zhuolin Jiang and Li,Shao-Fa and Gao,Dong-Fa}
}
@conference {11897,
	title = {Identifying and segmenting human-motion for mobile robot navigation using alignment errors},
	booktitle = {12th International Conference on Advanced Robotics, 2005. ICAR {\textquoteright}05. Proceedings},
	year = {2005},
	month = {2005/07//},
	pages = {398 - 403},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {This paper presents a new human-motion identification and segmentation algorithm, for mobile robot platforms. The algorithm is based on computing the alignment error between pairs of object images acquired from a moving platform. Pairs of images generating relatively small alignment errors are used to estimate the fundamental frequency of the object{\textquoteright}s motion. A decision criterion is then used to test the significance of the estimated frequency and to classify the object{\textquoteright}s motion. To verify the validity of the proposed approach, experimental results are shown on different classes of objects},
	keywords = {Computer errors, Educational institutions, Frequency estimation, human-motion identification, human-motion segmentation, HUMANS, Image motion analysis, Image segmentation, mobile robot navigation, Mobile robots, Motion estimation, Navigation, Object detection, robot vision, SHAPE},
	isbn = {0-7803-9178-0},
	doi = {10.1109/ICAR.2005.1507441},
	author = {Abd-Almageed, Wael and Burns,B. J and Davis, Larry S.}
}
@article {14230,
	title = {Motion segmentation using occlusions},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {27},
	year = {2005},
	month = {2005/06//},
	pages = {988 - 992},
	abstract = {We examine the key role of occlusions in finding independently moving objects instantaneously in a video obtained by a moving camera with a restricted field of view. In this problem, the image motion is caused by the combined effect of camera motion (egomotion), structure (depth), and the independent motion of scene entities. For a camera with a restricted field of view undergoing a small motion between frames, there exists, in general, a set of 3D camera motions compatible with the observed flow field even if only a small amount of noise is present, leading to ambiguous 3D motion estimates. If separable sets of solutions exist, motion-based clustering can detect one category of moving objects. Even if a single inseparable set of solutions is found, we show that occlusion information can be used to find ordinal depth, which is critical in identifying a new class of moving objects. In order to find ordinal depth, occlusions must not only be known, but they must also be filled (grouped) with optical flow from neighboring regions. We present a novel algorithm for filling occlusions and deducing ordinal depth under general circumstances. Finally, we describe another category of moving objects which is detected using cardinal comparisons between structure from motion and structure estimates from another source (e.g., stereo).},
	keywords = {3D motion estimation, algorithms, Artificial intelligence, CAMERAS, Computer vision, Filling, hidden feature removal, Image Enhancement, Image Interpretation, Computer-Assisted, image motion, Image motion analysis, Image segmentation, Layout, MOTION, Motion detection, Motion estimation, motion segmentation, Movement, Object detection, occlusion, occlusions, optical flow, ordinal depth, Pattern Recognition, Automated, Photography, Reproducibility of results, segmentation, Semiconductor device modeling, Sensitivity and Specificity, video analysis., Video Recording},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2005.123},
	author = {Ogale, A. S and Ferm{\"u}ller, Cornelia and Aloimonos, J.}
}
@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 {18468,
	title = {Multimodal 3-D tracking and event detection via the particle filter},
	booktitle = {IEEE Workshop on Detection and Recognition of Events in Video, 2001. Proceedings},
	year = {2001},
	month = {2001///},
	pages = {20 - 27},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Determining the occurrence of an event is fundamental to developing systems that can observe and react to them. Often, this determination is based on collecting video and/or audio data and determining the state or location of a tracked object. We use Bayesian inference and the particle filter for tracking moving objects, using both video data obtained from multiple cameras and audio data obtained using arrays of microphones. The algorithms developed are applied to determining events arising in two fields of application. In the first, the behavior of a flying echo locating bat as it approaches a moving prey is studied, and the events of search, approach and capture are detected. In a second application we describe detection of turn-taking in a conversation between possibly moving participants recorded using a smart video conferencing setup},
	keywords = {algorithms, APPROACH, audio data collection, audio signal processing, Bayesian inference, Bayesian methods, belief networks, CAMERAS, capture, conversation, echo, Educational institutions, Event detection, event occurrence, filtering theory, flying echo locating bat behaviour, Image motion analysis, inference mechanisms, Laboratories, microphone arrays, moving object tracking, moving participants, moving prey, multimodal 3D tracking, multiple cameras, Object detection, particle filter, Particle filters, Particle tracking, Robustness, search, smart video conferencing setup, target tracking, Teleconferencing, tracking filters, turn-taking detection, video data collection, video signal processing},
	isbn = {0-7695-1293-3},
	doi = {10.1109/EVENT.2001.938862},
	author = {Zotkin,Dmitry N and Duraiswami, Ramani and Davis, Larry S.}
}
@conference {18307,
	title = {3D object recognition via simulated particles diffusion},
	booktitle = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1989. Proceedings CVPR {\textquoteright}89},
	year = {1989},
	month = {1989/06//},
	pages = {442 - 449},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {A novel approach for 3D object recognition is presented. This approach is model-based, and assumes either 3D or 21/2 D scene acquisition. Transformation detection is accomplished along with an object identification (six degrees of freedom, three rotational and three translational, are assumed). The diffusion-like simulation recently introduced as a means for characterization of shape is used in the extraction of point features. The point features represent regions on the object{\textquoteright}s surface that are extreme in curvature (i.e. concavities and convexities). Object matching is carried out by examining the correspondence between the object{\textquoteright}s set of point features and the model{\textquoteright}s set of point features, using an alignment strategy. Triangles are constructed between all possible triples of object{\textquoteright}s point features, and then are aligned to candidate corresponding triangles of the model{\textquoteright}s point features. 21/2 range images are transformed into a volumetric representation through a parallel projection onto the 3-D space. The resultant volume is suitable for processing by the diffusion-like simulation},
	keywords = {3D object recognition, alignment strategy, Computational modeling, Computer science, data mining, Gold, Layout, Noise shaping, Object detection, Object recognition, parallel projection, pattern recognition, point features, radio access networks, scene acquisition, shape characterisation, Shape measurement, simulated particles diffusion, transformation detection},
	isbn = {0-8186-1952-x},
	doi = {10.1109/CVPR.1989.37886},
	author = {Yacoob,Yaser and Gold,Y. I}
}
