People localization in a camera network combining background subtraction and scene-aware human detection

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6523 LNCS, Issue PART 1, 2011, Pages 151-160

  Lee, Tung Ying ^a   Lin, Tsung Yu ^a   Huang, Szu Hao ^a   Lai, Shang Hong ^a   Hung, Shang Chih ^b  

^a NATIONAL TSING HUA UNIVERSITY   (Taiwan)

^b INDUSTRIAL TECHNOLOGY RESEARCH INSTITUTE   (Taiwan)

Author keywords

human localization; multi camera surveillance; Probabilistic occupancy map; video surveillance

Indexed keywords

BACKGROUND SUBTRACTION; CAMERA CALIBRATION; CAMERA NETWORK; CONVEX OPTIMIZATION PROBLEMS; GROUND PLANES; HUMAN DETECTION; HUMAN LOCALIZATION; IMAGE INFORMATION; IMAGE SIZES; LOCALIZATION PROBLEMS; MULTI-CAMERA SURVEILLANCE; MULTI-CAMERA VIDEO; MULTI-CAMERAS; NOVEL METHODS; PROBABILISTIC OCCUPANCY MAP; PROBLEM SIZE; THREE DIMENSIONAL SPACE; VIDEO SURVEILLANCE; HUMAN LOCALIZATIONS; MULTI-CAMERA VIDEO SURVEILLANCES;

CAMERAS; CONVEX OPTIMIZATION; MONITORING; OPTIMIZATION; VIDEO CAMERAS; ITERATIVE METHODS;

SECURITY SYSTEMS;

EID: 78751660645     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-17832-0_15     Document Type: Conference Paper

References (12)

1. Fleuret, F., Berclaz, J., Lengagne, R., Fua, P.: Multicamera people tracking with a probabilistic occupancy map. IEEE Trans. Pattern Analysis and Machine Intelligence 30, 267-282 (2008)
2. Delannay, D., Danhier, N., De Vleeschouwer, C.: Detection and recognition of sports(wo)men from multiple views. In: Third ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC), pp. 1-7 (2009)
3. Berclaz, J., Fleuret, F., Fua, P.: Multi-camera tracking and atypical motion detection with behavioral maps. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 112-125. Springer, Heidelberg (2008)
4. Levi, K., Weiss, Y.: Learning object detection from a small number of examples: The importance of good features. In: CVPR, vol. II, pp. 53-60 (2004)
5. Paisitkriangkrai, S., Shen, C., Zhang, J.: Fast pedestrian detection using a cascade of boosted covariance features. IEEE Trans. Circuits and Systems for Video Technolog 18, 1140-1151 (2008)
6. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR,vol. I, pp. 886-893 (2005)
7. Chen, Y., Chen, C.: Fast human detection using a novel boosted cascading structure with meta stages. IEEE Trans. Image Processing 17, 1452-1464 (2008)
8. Tuzel, O., Porikli, F., Meer, P.: Pedestrian detection via classification on riemannian manifolds. IEEE Trans. Pattern Analysis and Machine Intelligence 30, 1713-1727 (2008)
9. Alahi, A., Boursier, Y., Jacques, L., Vandergheynst, P.: A sparsity constrained inverse problem to locate people in a network of cameras. In: 16th International Conference on Digital Signal Processing, pp. 1-7 (2009)
10. Alahi, A., Boursier, Y., Jacques, L., Vandergheynst, P.: Sport players detection and tracking with a mixed network of planar and omnidirectional cameras. In: Third ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC), pp. 1-8 (2009)
11. Kaewtrakulpong, P., Bowden, R.: An improved adaptive background mixture model for realtime tracking with shadow detection. In: Proc. 2nd European Workshop on Advanced Video Based Surveillance Systems (2001)
12. Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: CVPR, pp. II: 246-252 (1999)