Abstract
This chapter presents a novel system for pedestrian surveillance, including tasks such as detection, tracking, classification, and possibly activity analysis. The system we propose first builds a background model as a multimodal distribution of colors and temperatures. It then constructs a particle filter scheme that makes a number of informed reversible transformations to sample the model probability space to maximize posterior probability of the scene model. Observation likelihoods of moving objects account their three-dimensional locations with respect to the camera and occlusions by other tracked objects as well as static obstacles. After capturing the coordinates and dimensions of moving objects, we apply a classifier based on periodic gait analysis. To differentiate humans from other moving objects such as cars, we detect a symmetrical double-helical pattern in human gait. Such pattern can then be analyzed using the frieze group theory. The results of tracking in color and thermal sequences demonstrate that our algorithm is robust to illumination change and performs well in outdoor environments.
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Chapter's References
B. Bhanu and J. Han. Kinematic-based human motion analysis in infrared sequences. In IEEE Workshop on Applications of Computer Vision, pages 208–212, 2002
R. Collins, A. Lipton, and T. Kanade. Introduction to the special section on video surveillance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8):745–746, 2000
R. Collins, A. Lipton, T. Kanade, H. Fujiyoshi, D. Duggins, Y. Tsin, D. Tolliver, N. Enomoto, O. Hasegawa, P. Burt, and L. Wixson. A system for video surveillance and monitoring. Technical Report CMU-RI-TR-00-12, Carnegie Mellon University, Pittsburgh, May 2000
C. Dai, Y. Zheng, and X. Li. Layered representation for pedestrian detection and tracking in infrared imagery. In IEEE CVPR Workshop on OTCBVS, 2005
J. Davis and V. Sharma. Fusion-based background-subtraction using contour saliency. In IEEE International Workshop on Object Tracking and Classification beyond the Visible Spectrum, IEEE OTCBVS WS Series Bench, 2005
J. W. Davis and V. Sharma. Fusion-based background-subtraction using contour saliency. In IEEE CVPR WS on Object Tracking and Classification Beyond the Visible Spectrum, pages 19–26, 2005
J. Deutscher, B. North, B. Bascle, and A.B. Blake. Tracking through singularities and discontinuities by random sampling. In International Conference on Computer Vision, 1999
A. Doucet, N. de Freitas, and N. Gordon. Sequential Monte Carlo Methods in Practice. Springer-Verlag, New York, 2001
A. Elgammal and L. Davis. Probabilistic framework for segmenting people under occlusion. In International Conference on Computer Vision, 2001
D. Gavrila. The visual analysis of human movement: A survey. Computer Vision and Image Understanding, 73(1):82–98, 1999
I. Haritaoglu and M. Flickner. Detection and tracking of shopping groups in stores. In International Conference on Computer Vision and Pattern Recognition, 2001
I. Haritaoglu, D. Harwood, and L. Davis. W-4: Real-time surveillance of people and their activities. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8):809–830, 2000
L. Havasi and T. Sziranyi. Motion tracking through grouped transient feature points. In Advanced Concepts for Intelligent Vision Systems, 2004
A.Z.R. Hartley. Multiple View Geometry in Computer Vision. Part 2. Cambridge University Press, Cambridge, UK, 2004
M. Isard and J. MacCormick. Bramble: A Bayesian multiple-blob tracker. In International Conference on Computer Vision, 2:34–41, 2001
C. Kemp and T. Drummond. Multi-modal tracking using texture changes. Image and Vision Computing Volume 26, Issue 3 (March 2008). Pages: 442–450
A. Leykin. Visual Human Tracking and Group Activity Analysis: A Video Mining System for Retail Marketing. Ph.D. thesis, Indiana University, 2007
A. Leykin and R. Hammoud. Robust multi-pedestrian tracking in thermal-visible surveillance videos. Computer Vision and Pattern Recognition Workshop (cvprw), Volume, Issue 17–22, 136–136,, 2006
J. Little and J. Boyd. Recognizing people by their gait: The shape of motion. Journal of Computer Vision Research, 1(2):1–32, 1998
F. Liu and R. Picard. Finding periodicity in space and time. Proceedings of the Sixth International Conference on Computer Vision, pages 376–382, 1998
S. Maybank and T. Tan. Introduction to special section on visual surveillance. International Journal of Computer Vision, 37(2):173–173, 2000
A. Mittal and L. Davis. M2tracker: A multi-view approach to segmenting and tracking people in a cluttered scene using region-based stereo. In European Conference on Computer Vision, 2002
D. Morris and J. Rehg. Singularity analysis for articulated object tracking. In International Conference on Computer Vision and Pattern Recognition, 1998
C.S. Myers and L.R. Rabiner. A comparative study of several dynamic time-warping algorithms for connected word recognition. Bell System Technical Journal, 60(7):1389–1409, 1981
H. Nanda and L. Davis. Probabilistic template based pedestrian detection in infrared videos. In IEEE Intelligent Vehicles Symposium, 2002
S. Niyogi and E. Adelson. Analyzing and recognizing walking figures in xyt. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pages 469–474, 1994
N. Oliver, B. Rosario, and A. Pentland. A Bayesian computer vision system for modeling human interactions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8):831–843, 2000
Y. Owechko, S. Medasani, and N. Srinivasa. Classifier swarms for human detection in infrared imagery. In IEEE CVPR on Object Tracking and Classification Beyond the Visible Spectrum, 2004
I.Y. Ran, Q. Zheng and L.S. Davis. Pedestrian classifcation from moving platforms using cyclic motion pattern. International Conference of Image Processing, pages 854–857, 2005
Y. Ran, Q.Z.I. Weiss, and L.S. Davis. Pedestrian detection via periodic motion analysis. Interenational Journal of computer Vision, 2006
C. Sminchisescu and B. Triggs. Kinematic jump processes for monocular 3D human tracking. In International Conference on Computer Vision and Pattern Recognition, 2003
W.H.L. Wang and T. Tan. Recent developments in human motion analysis. Pattern Recognition, 36(3):585–601, 2003
F. Xu and K. Fujimura. Pedestrian detection and tracking with night vision. In IEEE Intelligent Vehicles Symposium, 2002
M. Yasuno, N. Yasuda, and M. Aoki. Pedestrian detection and tracking in far infrared images. In IEEE CVPR Workshop on Object Tracking and Classification Beyond the Visible Spectrum, 2004
T. Zhao and R. Nevatia. Tracking multiple humans in crowded environment. In International Conference on Computer Vision and Pattern Recognition, 2004.
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Ran, Y., Leykin, A., Hammoud, R. (2009). Thermal-Visible Video Fusion for Moving Target Tracking and Pedestrian Motion Analysis and Classification. In: Hammoud, R.I. (eds) Augmented Vision Perception in Infrared. Advances in Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-84800-277-7_15
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DOI: https://doi.org/10.1007/978-1-84800-277-7_15
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