@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}
}
