Pedestrian recognition in far-infrared images by combining boosting-based detection and skeleton-based stochastic tracking

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

Volumn 4319 LNCS, Issue , 2006, Pages 483-494

  Miyamoto, Ryusuke ^a,b   Sugano, Hiroki ^a,b   Saito, Hiroaki ^c   Tsutsui, Hiroshi ^a   Ochi, Hiroyuki ^a,b   Hatanaka, Ken'ichi ^c   Nakamura, Yukihiro ^a,b  

^a KYOTO UNIVERSITY   (Japan)

^b Kyoto Center   (Japan)

^c SUMITOMO ELECTRIC INDUSTRIES LTD   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

MUSCULOSKELETAL SYSTEM; NIGHT VISION DEVICES; STOCHASTIC SYSTEMS;

ACCURATE TRACKING; CONVENTIONAL SCHEMES; FALSE POSITIVE; HIGHLY ACCURATE; NIGHT VISION SYSTEMS; PEDESTRIAN RECOGNITION; STOCHASTIC TRACKING; VISIBLE IMAGE;

INFRARED IMAGING;

EID: 70350270332     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11949534_48     Document Type: Conference Paper

References (23)

1. Xu, F., Fujimura, K.: Pedestrian detection and tracking with night vision. IEEE Trans. on ITS 6 (2005) 63-71
2. T, T., Hattori, H., Watanabe, M., Nagaoka, N.: Development of night-vision system. IEEE Trans. on ITS 3 (2002) 203-209
3. Fang, Y., Yamada, K., Ninomiya, Y., Horn, B.K.P., Masaki, I.: A shape-independent method for pedestrian detection with far-infrared images. IEEE Trans. on VT 53 (2004) 1679-1697
4. Liu, X., Fujimura, K.: Pedestrian detection using stereo night vision. IEEE Trans. on VT 53 (2004) 1657-1665
5. Yasuno, M., Yasuda, N., Aoki, M.: Pedestrian detection and tracking in far infrared images. In: Proc. of CVPRW. (2004) 125-125
6. Sun, H., Hua, C., Luo, Y.: A multi-stage classifier based algorithm of pedestrian detection in night with a near infrared camera in a moving car. In: Proc. of ICIG. (2004) 120-123
7. Bertozzi, M., Broggi, A., Fascioli, A., Graf, T., Meinecke, M.M.: Pedestrian detection for driver assistance using multiresolution infrared vision. IEEE Trans. on VT 53 (2004) 1666-1678
8. Oren, M., Papageorgiou, C., Sinha, P., Osuna, E., Proggio, T.: Pedestrian detection using wavelet templates. In: Proc. of CVPR. (1997) 193-199
9. Papageorgiou, C., Poggio, T.: Trainable pedestrian detection. In: Proc. of ICIP. Volume 4. (1999) 35-39
10. Soga, M., Kato, T., Ohta, M., Ninomiya, Y.: Pedestrian detection using stereo vision and tracking. In: Proc. of The 11th World Congress on Intelligent Transport Systems. (2004)
11. Zhao, L., Thorpe, C.E.: Stereo- and neural network-based pedestrian detection. IEEE Trans. on ITS 01 (2000) 148-154
12. Viola, P., Jones, M.J., Snow, D.: Detecting pedestrians using patterns of motion and appearance. International Journal of Computer Vision 63 (2005) 153-161
13. Hu, W., Tan, T., Wang, L., Maybank, S.: A survey on visual surveillance of object motion and behaviors. IEEE Trans. on SMC 34 (2004) 334-352
14. Bertozzi, M., Broggi, A., Fascioli, A., Tibaldi, A., Chapuis, R., Chausse, F.: Pedestrian localization and tracking system with kalman filtering. In: Proc. of IEEE Intelligent Vehicles Symposium. (2004) 584-589
15. Isard, M., Blake, A.: Condensation-conditional density propagation for visual tracking. International Journal of Computer Vision 29(1) (1998) 5-28
16. Sidenbladh, H., Black, M.J., Fleet, D.J.: Stochastic tracking of 3D human figures using 2D image motion. In: Proc. of ECCV. (2000) 1-18
17. Sigal, L., Bhatia, S., Roth, S., Black, M.J., Isard, M.: Tracking loose-limbed people. In: Proc. of CVPR. Volume 1. (2004) 421-428
18. Balan, A.O., Sigal, L., Black, M.J.: A quantitative evaluation of video-based 3D person tracking. In: Proc. of IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance. (2005) 349-356
19. Kitagawa, G.: Monte-carlo filter and smoother for nongaussian nonlinear state space models. Journal of Computational and Graphical Statistics 5(1) (1996) 1-25
20. Ashida, J., Miyamoto, R., Tsutsui, H., Onoye, T., Nakamura, Y.: Probabilistic pedestrian tracking based on a skeleton model. In: Proc. of ICIP. (2006) to appear.
21. Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55(1) (1997) 119-139
22. Miyamoto, R., Ashida, J., Tsutsui, H., Nakamura, Y.: Skeleton based stochastic pedestrian tracking for surveillance. In: Proc. of The 10th WMSCI. Volume V. (2006) 206-211
23. Matsumoto, M., Nishimura, T.: Mersenne twister: A 623-dimensionally equidistributed uniform pseudorandom number generator. ACM Trans. on Modeling and Computer Simulation 8 (1998) 3-30