Human detection and tracking for video surveillance applications in low-density environment

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 5150 I, Issue , 2003, Pages 51-60

  Carminati, Lionel ^a   Benois Pineau, Jenny ^a   Gelgon, Marc ^b  

^a UNIVERSITÉ BORDEAUX 1   (France)

^b IRIN/ATLAS INRIA Team   (France)

Author keywords

Block matching; Gaussian mixture; Motion based detection; Motion estimation; Real time tracking; Support vector machine

Indexed keywords

EDGE DETECTION; IMAGE ANALYSIS; MOTION ESTIMATION; NEURAL NETWORKS; OBJECT RECOGNITION; PATTERN MATCHING; PERSONAL COMPUTERS; REAL TIME SYSTEMS; STATISTICAL METHODS;

BLOCK MATCHING; MOTION BASED DETECTION; SUPPORT VECTOR MACHINE; VIDEO SURVEILLANCE SYSTEM;

PATTERN RECOGNITION SYSTEMS;

EID: 0141831260     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.509865     Document Type: Conference Paper

References (18)

1. I. Mikic, S. Santini, and R.Jain, "Video processing and integration from multiple camera," Image Understanding Workshop 1, pp. 183-187, 1998.
2. W. Grimson, C. Stauffer, R. Romano, and L.Lee, "Using adaptive tracking to classify and monitor activities in a site," IEEE CVPR 1998, pp. 22-29, 1998.
3. T. Kanade, R. Collins, A. Lipton, P. Burt, and L. Wixson, "Advances in cooperatives multi-sensor video surveillance," in Proceedings of DARPA Image Understanding Workshop, 1, pp. 3-24, 1998.
4. V. Vapnik, The Nature of Statistical Learning Theory, Springer-Verlag, New york, ISBN 0-387-94559-8, 1995.
5. H. Rowley, S. Baluja, and K. Takeo, "Neural network-based face detection," IEEE PAMI 1999, pp. 20:23-38, 1999.
6. H. Schneiderman and T. Kanade, "Probabilistic modeling of local appearance and spatial relationship for object recognition," in IEEE CVPR 1998, pp. 45-51, 1998.
7. F. Fleuret and J.M. Vzien, "Face detection from video stream using decision trees," INRIA Technical Report, pp. 1-10, 1999.
8. C. J. Burges, "A tutorial on support vector machine for pattern recognition," Data-Mining and Knowledge Discovery 2, pp. 121-167, 1998.
9. E. Osuna, R. Freund, and F. Girosi, "Support vector machines: Training and applications," Tech. Rep. AIM-1602, 1997.
10. A. Mahboubi, J. Benois-Pineau, and D. Barba, "Tracking of objects in video scenes with time varying content," EURASIP Journal on Applied Signal Processing 2002, pp. 582-594, June 2002.
11. T. Koga, K. Iinuma, A. Hirano, Y. Iijima, and T.Ishiguro, "Motion-compensated interframe coding for video conferencing," Proceedings NTC 81, pp. pp C9.6.1-9.6.5, November 1981.
12. S. Jehan-Besson, M. Barlaud, and G. Aubert, "An object-based method for video coding," Proceedings IEEE ICIP 2001, pp. 502-505, September 2001.
13. C. Stauffer and W. Grimson, "Adaptive background mixture models for real-time tracking," The Artificial Interlligence Laboratory, Massachussets, Institute of Technology Cambridge, MA02139, 1999.
14. A. Dempster, N. Laird, and D. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," Journal of the Royal Statistical Society, pp. 39:1-38 Series B, 1977.
15. J. Besag, "Spatial interaction and the statistical analysis of lattice systems," Journal of the Royal Statistical Society, pp. 36:192-236 Series B, 1974.
16. M.I.T. SvmFu Version 3 Software developed by the Center for Biological and Computational Learning du M.I.T. http://five-percent-nation.mit.edu/SvmFu, 2001.
17. M.I.T. CBCL Face Data Set http://www.ai.mit.edu/projects/cbcl/software-datasets/FaceData2.html, 2000.
18. OpenVideo Project, "http://www.open-video.org" Open Video Project is a shared digital video repository and test collection intended to meet the needs of researchers in a wide variety of areas related to digital video., 2001.