A new algorithm for robust pedestrian tracking based on manifold learning and feature selection

IEEE Transactions on Intelligent Transportation Systems

Volumn 12, Issue 4, 2011, Pages 1195-1208

  Wang, Min ^a   Qiao, Hong ^a   Zhang, Bo ^b  

^a INSTITUTE OF AUTOMATION   (China)

^b INSTITUTE OF APPLIED MATHEMATICS   (China)

Author keywords

Feature extraction; manifold learning; tracking

Indexed keywords

CLASSIFICATION AND RECOGNITION; COLOR FEATURES; COMPLEX BACKGROUND; DIMENSIONALITY REDUCTION; FEATURE REPRESENTATION; FOREGROUND EXTRACTION; INTRINSIC FEATURES; MANIFOLD LEARNING; NOVEL ALGORITHM; PEDESTRIAN TRACKING;

LEARNING ALGORITHMS; SURFACE DISCHARGES;

FEATURE EXTRACTION;

EID: 82455167975     PISSN: 15249050     EISSN: None     Source Type: Journal    
DOI: 10.1109/TITS.2011.2148717     Document Type: Article

References (35)

1. I. P. Alonso, D. F. Llorca, M. A. Sotelo, L. M. Bergasa, P. R. de Toro, J. Nuevo, M. Ocana, and M. A. Garrido, "Combination of feature extraction methods for SVM pedestrian detection," IEEE Trans. Intell. Transp. Syst., vol. 8, no. 2, pp. 292-307, Jun. 2007.
2. S. Munder, C. Schnorr, and D. M. Gavrila, "Pedestrian detection and tracking using a mixture of view-based shape-texture models," IEEE Trans. Intell. Transp. Syst., vol. 9, no. 2, pp. 333-343, Jun. 2008.
3. N. T. Siebel and S. Maybank, "Fusion of multiple tracking algorithms for robust people tracking," in Proc. 7th Eur. Conf. Comput. Vis., Copenhagen, Denmark, May 2002, pp. 373-387.
4. Y.Wu and T. Yu, "A field model for human detection and tracking," IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 5, pp. 753-765, May 2006.
5. H. Qiao, P. Zhang, B. Zhang, and S. W. Zheng, "Learning an intrinsic variable preserving manifold for dynamic visual tracking," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 40, no. 3, Sp. Iss. SI, pp. 868-880, Jun. 2010.
6. W. K. Liao and G. Medioni, "3D face tracking and expression inference from a 2D sequence using manifold learning," in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Anchorage, AK, Jun. 23-28, 2008, pp. 1-8.
7. T. Jaeggli, E. Koller-Meier, and L. Van Gool, "Learning generative models formulti activity body pose estimation," Int. J. Comput. Vis., vol. 83, no. 2, pp. 121-134, Jun. 2009.
8. A. Elgammal and C. S. Lee, "Tracking people on a torus," IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 3, pp. 520-538, Mar. 2009.
9. J. B. Tenenbaum, V. de Silva, and J. C. Langford, "A global geometric framework for nonlinear dimensionality reduction," Science, vol. 290, no. 5500, pp. 2319-2323, Dec. 2000.
10. S. T. Roweis and L. K. Saul, "Nonlinear dimensionality reduction by locally linear embedding," Science, vol. 290, no. 5500, pp. 2323-2326, Dec. 2000.
11. M. Belkin and P. Niyogi, "Laplacian eigenmaps for dimensionality reduction and data representation," Neural Comput., vol. 15, no. 6, pp. 1373-1396, Jun. 2003.
12. K. Q. Weinberger, F. Sha, and L. K. Saul, "Learning a kernel matrix for nonlinear dimensionality reductio," in Proc. 21st Int. Conf. Mach. Learn., 2004, pp. 106-113.
13. K. Moon and V. Pavlovic, "Impact of dynamics on subspace embedding and tracking of sequences," in Proc. IEEE CS Conf. Comput. Vis. Pattern Recog., 2006, pp. 198-205.
14. A. Rahimi, B. Recht, and T. Darrell, "Learning appearance manifolds from video," in Proc. IEEE CS Conf. Comput. Vis. Pattern Recog., 2005, vol. 1, pp. 868-875.
15. A. Elgammal and C. S. Lee, "Inferring 3D body pose from silhouettes using activity manifold learning," in Proc. IEEE CS Conf. Comput. Vis. Pattern Recog., 2004, vol. 2, pp. 681-688.
16. H. Lim, O. I. Camps, M. Sznaier, and V. I. Morariu, "Dynamic appearance modeling for human tracking," in Proc. IEEE CS Conf. Comput. Vis. Pattern Recog., 2006, pp. 751-757.
17. C. Sminchisescu and A. Jepson, "Generative modeling of continuous nonlinearly embedded visual inference," in Proc. Int. Conf. Mach. Learn., 2004, pp. 140-147.
18. X. F. He, S. C. Yan, Y. X. Hu, P. Niyogi, and H. J. Zhang, "Face recognition using Laplacianfaces," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 3, pp. 328-340, Mar. 2005.
19. L. Wang and D. Suter, "Learning and matching of dynamics shape manifolds for human action recognition," IEEE Trans. Image Process., vol. 16, no. 6, pp. 1646-1661, Jun. 2007.
20. D. Cai, X. F. He, J. W. Han, and H. J. Zhang, "Orthogonal Laplacianfaces for face recognition," IEEE Trans. Image Process., vol. 15, no. 11, pp. 3608-3614, Nov. 2006.
21. S. W. Zheng, H. Qiao, B. Zhang, and P. Zhang, "The application of intrinsic variable preserving manifold learning method to tracking multiple people with occlusion reasoning," in Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., 2009, pp. 2993-2998.
22. R. T. Collins, Y. X. Liu, and M. Leordeanu, "Online selection of discriminative tracking features," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 10, pp. 1631-1643, Oct. 2005.
23. J. Wang and Y. Yagi, "Intergrating color and shape-texture features for adaptive real-time object tracking," IEEE Trans. Image Process., vol. 17, no. 2, pp. 235-240, Feb. 2008.
24. H. Stern and B. Efros, "Adaptive color space switching for face tracking in multi-colored lighting environment," in Proc. 5th IEEE Int. Conf. Autom. FGR, 2002, pp. 249-254.
25. A. D. Jepson, D. J. Fleet, and T. El-Maraghi, "Robust online appearance models for visual tracking," IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 10, pp. 1296-1311, Oct. 2003.
26. C. Wang, J. P. Zhang, J. A. Pu, X. R. Yuan, and L. Wang, "Chrono-gait image: A novel temporal template for gait recognition," in Proc. 11th Eur. Conf. Comput. Vis., 2010, pp. 257-270.
27. P. Wang and H. Qiao, "Online appearance model learning and generation for adaptive visual tracking," IEEE Trans. Circuits Syst. Video Technol., vol. 21, no. 2, pp. 156-169, Feb. 2011.
28. P. Viola, M. Jones, and D. Snow, "Detecting pedestrians using patterns of motion and appearance," in Proc. Int. Conf. Comput. Vis., 2003, vol. 2, pp. 734-741.
29. D. Comaniciu, V. Ramesh, and P. Meer, "Kernel-based object tracking," IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 5, pp. 564-577, May 2003.
30. M. Isard and A. Blake, "Condensation-Conditional density propagation for visual tracking," Int. J. Comput. Vis., vol. 29, no. 1, pp. 5-28, 1998.
31. F. L. Xu, X. Liu, and K. Fujimura, "Pedestrian detection and tracking with night vision," IEEE Trans. Intell. Transp. Syst., vol. 6, no. 1, pp. 63-71, Mar. 2005.
32. Y. Zhu, D. Comaniciu,M. Pellkofer, and T. Koehler, "Reliable detection of overtaking vehicles using robust information fusion," IEEE Trans. Intell. Transp. Syst., vol. 7, no. 4, pp. 401-414, Dec. 2006.
33. S. Gidel, P. Checchin, C. Blanc, T. Chateau, and L. Trassoudaine, "Pedestrian detection and tracking in an urban environment using a multilayer laser scanner," IEEE Trans. Intell. Transp. Syst., vol. 11, no. 3, pp. 579-588, Sep. 2010.
34. S. Sivaraman and M. M. Trivedi, "A general active-learning framework for on-road vehicle recognition and tracking," IEEE Trans. Intell. Transp. Syst., vol. 11, no. 2, pp. 267-276, Jun. 2010.
35. K. Okuma, A. Taleghani, N. de Freitas, J. J. Little, and D. G. Lowe, "A boosted particle filter: Multitarget detection and tracking," in Proc. 8th Eur. Conf. Comput. Vis., Prague, Czech Republic, May 11-14, 2004, pp. 28-39.