Pedestrian tracking by fusion of thermal-visible surveillance videos

Machine Vision and Applications

Volumn 21, Issue 4, 2010, Pages 587-595

  Leykin, Alex ^a   Hammoud, Riad ^b  

^a INDIANA UNIVERSITY   (United States)

^b DELPHI CORPORATION   (United States)

Author keywords

Fusion of color and thermal imagery; Thermal cameras; Visual tracking

Indexed keywords

BACKGROUND MODEL; HUMAN BODIES; MODEL POSTERIORS; MODEL PROBABILITIES; MULTI-MODAL; OUTDOOR ENVIRONMENT; PARTICLE FILTER; PEDESTRIAN TRACKING; PROBABILISTIC MODELS; REVERSIBLE TRANSFORMATION; STATIC OBJECTS; SURVEILLANCE VIDEO; THERMAL CAMERA; THERMAL IMAGERY; VISUAL TRACKING;

CAMERAS; COLOR; PROBABILITY DISTRIBUTIONS;

SECURITY SYSTEMS;

EID: 77956617480     PISSN: 09328092     EISSN: 14321769     Source Type: Journal    
DOI: 10.1007/s00138-008-0176-5     Document Type: Article

References (33)

1. Bhanu, B., Han, J.: Kinematic-based human motion analysis in infrared sequences. In: IEEE WS. on Applications of Computer Vision, pp. 208-212 (2002)
2. CAVIAR: 1st 37540. Found at http://homepages.inf.ed.ac.uk/rbf/CAVIAR/ (2001)
3. Collins, R., Lipton, A., Kanade, T.: Introduction to the special section on video surveillance. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 745-746 (2000)
4. Collins, R., Lipton, A., Kanade, T., Fujiyoshi, H., Duggins, D., Tsin, Y., Tolliver, D., Enomoto, N., Hasegawa, O., Burt, P., Wixson, L.: A system for video surveillance and monitoring. Tech. Rep. CMU-RI-TR-00-12. Carnegie Mellon University, Pittsburgh (2000)
5. Connaire, C.O., O'Connor, N., Smeaton., A.F.: Thermo-visual feature fusion for object tracking using multiple spatiogram trackers. Mach. Vis. Appl., pp. 1-12 (2007). doi:10.1007/ s00138-007-0078-y
6. Dai, C., Zheng, Y., Li, X.: Layered representation for pedestrian detection and tracking in infrared imagery. In: IEEE CVPR WS on OTCBVS (2005)
7. Davis, J., Sharma, V.: 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)
8. Davis, J.W., Sharma, V.: Fusion-based background-subtraction using contour saliency. In: IEEE CVPR WS on Object Tracking and Classification Beyond the Visible Spectrum, pp. 19-26 (2005)
9. Deutscher, J., North, B., Bascle, B., Blake, A.b.: Tracking through singularities and discontinuities by random sampling. In: International Conference on Computer Vision (1999)
10. Doucet, A., de Freitas, N., Gordon, N.: Sequential Monte Carlo Methods in Practice. Springer, Heidelberg (2001)
11. Elgammal, A., Davis, L.: Probabilistic framework for segmenting people under occlusion. In: International Conference on Computer Vision (2001)
12. Gavrila, D.: The visual analysis of human movement: A survey. Comp. Vis. Image Understanding 73(1), 82-98 (1999)
13. Haritaoglu, I., Flickner, M.: Detection and tracking of shopping groups in stores. In: International Conference on Computer Vision and Pattern Recognition (2001)
14. Haritaoglu, I., Harwood, D., Davis, L.: W-4: Real-time surveillance of people and their activities. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 809-830 (2000)
15. Havasi, L., Sziranyi, T.: Motion tracking through grouped transient feature points. In: Advanced Concepts for Intelligent Vision Systems (2004)
16. Isard, M., MacCormick, J.: Bramble: A bayesian multiple-blob tracker. In: International Conference on Computer Vision (2001)
17. Kemp, C., Drummond, T.: Multi-modal tracking using texture changes. In: British Machine Vision Conference (2004)
18. Kim, K., Chalidabhongse, T., Harwood, D., Davis, L.: Background modeling and subtraction by codebook construction. In: International Conference on Image Processing (2004)
19. Leykin, A.: Visual human tracking and group activity analysis: A video mining system for retail marketing. PhD Thesis, Indiana University (2007)
20. Leykin, A., Hammoud, R.: Robust multi-pedestrian tracking in thermal-visible surveillance videos. In: IEEE CVPR WS on Object Tracking and Classification Beyond the Visible Spectrum, p. 136. IEEE Computer Society, Los Alamitos (2006)
21. Leykin, A., Tuceryan, M.: A vision system for automated customer tracking for marketing analysis: Low level feature extraction. In: Human Activity Recognition and Modelling Workshop (2005)
22. Maybank, S., Tan, T.: Introduction to special section on visual surveillance. Int. J. Comp. Vis. 37(2), 173-173 (2000)
23. Mittal, A., Davis, L.: 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)
24. Morris, D., Rehg, J.: Singularity analysis for articulated object tracking. In: International Conference on Computer Vision and Pattern Recognition (1998)
25. Nanda, H., Davis, L.: Probabilistic template based pedestrian detection in infrared videos. In: IEEE Intell. Vehicles Symp. (2002)
26. Oliver, N., Rosario, B., Pentland, A.: A bayesian computer vision system for modeling human interactions. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 831-843 (2000)
27. Owechko, Y., Medasani, S., Srinivasa, N.: Classifier swarms for human detection in infrared imagery. In: IEEE CVPR WS on Object Tracking and Classification Beyond the Visible Spectrum (2004)
28. Rana, Y., Weiss, I., Zheng, Q., Davis, L.S.: Pedestrian detection via periodic motion analysis. Int. J. Comp. Vis. 71(2), 143-160 (2007)
29. Rittscher, J., Tu, P., Krahnstoever, N.: Simultaneous estimation of segmentation and shape. In: International Conference on Computer Vision and Pattern Recognition, vol. II, pp. 486-493 (2005)
30. Sminchisescu, C., Triggs, B.: Kinematic jump processes for monocular 3d human tracking. In: International Conference on Computer Vision and Pattern Recognition (2003)
31. Xu, F., Fujimura, K.: Pedestrian detection and tracking with night vision. In: IEEE Intell. Vehicles Symp. (2002)
32. Yasuno, M., Yasuda, N., Aoki, M.: Pedestrian detection and tracking in far infrared images. In: IEEE CVPR WS on Object Tracking and Classification Beyond the Visible Spectrum (2004)
33. Zhao, T., Nevatia, R.: Tracking multiple humans in crowded environment. In: International Conference on Computer Vision and Pattern Recognition (2004)