Vehicle identification via sparse representation

IEEE Transactions on Intelligent Transportation Systems

Volumn 13, Issue 2, 2012, Pages 955-962

  Wang, Shuang ^a   Cui, Lijuan ^a   Liu, Dianchao ^a   Huck, Robert ^a   Verma, Pramode ^a   Sluss Jr , James J ^a   Cheng, Samuel ^a  

^a University of Oklahoma   (United States)

Author keywords

Sparse representation; vehicle identification

Indexed keywords

COMPRESSIVE SENSING; IN-VEHICLE; MULTIPLE LINEAR REGRESSION MODELS; SPARSE REPRESENTATION; VEHICLE IDENTIFICATION;

FEATURE EXTRACTION;

LINEAR REGRESSION;

EID: 84861901780     PISSN: 15249050     EISSN: None     Source Type: Journal    
DOI: 10.1109/TITS.2011.2171034     Document Type: Article

References (30)

1. N. Friedman and S. Russell, "Image segmentation in video sequences: A probabilistic approach," in Proc. 13th Conf. UAI, 1997, pp. 175-181.
2. C. Stauffer and W. Grimson, "Adaptive background mixture models for real-Time tracking," in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 1999, vol. 2, pp. 246-252.
3. K. Karmann and A. von Brandt, "Moving object recognition using an adaptive background memory," in Proc. Time-Varying Image Process. Moving Object Recog., 1990, vol. 2, pp. 289-296.
4. D. Ramesh and P. Meer, "Real-Time tracking of non-rigid objects using mean shift," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2000, vol. 2, pp. 142-149.
5. S. Sivaraman and M. Trivedi, "A general active-learning framework for onroad vehicle recognition and tracking," IEEE Trans. Intell. Transport Syst., vol. 11, no. 2, pp. 267-276, Jun. 2010.
6. A. Jazayeri, H. Cai, J. Zheng, and M. Tuceryan, "Motion based vehicle identification in car video," in Proc. IEEE Intell. Veh. Symp., 2010, pp. 493-499.
7. A. Jazayeri, H. Cai, J. Zheng, and M. Tuceryan, "Vehicle detection and tracking in car video based on motion model," IEEE Trans. Intell. Transport. Syst., vol. 12, no. 2, pp. 583-595, Jun. 2011.
8. Z. Sun, G. Bebis, and R. Miller, "On-road vehicle detection using Gabor filters and support vector machines," in Proc. Int. Conf. Digit. Signal Process., 2002, vol. 2, pp. 1019-1022.
9. N. Matthews, P. An, D. Charnley, and C. Harris, "Vehicle detection and recognition in greyscale imagery," Control Eng. Pract., vol. 4, no. 4, pp. 473-479, Apr. 1996. (Pubitemid 126398129)
10. B. Morris and M. Trivedi, "Learning, modeling, and classification of vehicle track patterns from live video," IEEE Trans. Intell. Transp. Syst., vol. 9, no. 3, pp. 425-437, Sep. 2008.
11. Z. Qin, "Method of vehicle classification based on video," in Proc. IEEE/ASME Int. Conf. AIM, 2008, pp. 162-164.
12. N. Zeng and J. Crisman, "Vehicle matching using color," in Proc. IEEE Conf. Intell. Transp. Syst., 1997, pp. 206-211.
13. G. Kogut and M. Trivedi, "Maintaining the identity of multiple vehicles as they travel through a video network," in Proc. IEEEWorkshop Multi-Object Tracking, 2001, pp. 29-34.
14. A. MacCarley, Video-Based Vehicle Signature Analysis and Tracking Phase 1: Verification of Concept Preliminary Testing. Berkeley, CA: Univ. California, 1998.
15. C. MacCarley, Video-Based Vehicle Signature Analysis and Tracking System Phase 2: Algorithm Development and Preliminary Testing. Berkeley, CA: Univ. California, 2001.
16. J. Wright, A. Yang, A. Ganesh, S. Sastry, and Y. Ma, "Robust face recognition via sparse representation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 2, pp. 210-227, Feb. 2008.
17. L. Li, H. Su, E. Xing, and L. Fei-Fei, "Object bank: A high-level image representation for scene classification and semantic feature sparsification," in Proc. NIPS, Vancouver, BC, Canada, 2010.
18. X. Mei and H. Ling, "Robust visual tracking and vehicle classification via sparse representation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 11, pp. 2259-2272, Nov. 2011.
19. J. Thiagarajan, K. Ramamurthy, and A. Spanias, "Sparse representations for pattern classification using learned dictionaries," in Proc. 28th SGAI Int. Conf. Artif. Intell., 2008, pp. 33-45.
20. A. Senior, A. Hampapur, Y. Tian, L. Brown, S. Pankanti, and R. Bolle, "Appearance models for occlusion handling," Proc. Image Vis. Comput., vol. 24, no. 11, pp. 1233-1243, 2006. (Pubitemid 44485267)
21. A. Verri and T. Poggio, "Motion field and optical flow: Qualitative properties," IEEE Trans. Pattern Anal. Mach. Intell., vol. 11, no. 5, pp. 490-498, May 1989.
22. J. Tropp and A. Gilbert, "Signal recovery from random measurements via orthogonal matching pursuit," IEEE Trans. Inf. Theory, vol. 53, no. 12, pp. 4655-4666, Dec. 2007.
23. P. Zhao and B. Yu, "On model selection consistency of Lasso," J. Mach. Learn. Res., vol. 7, pp. 2541-2563, Dec. 2006. (Pubitemid 44866738)
24. F. Potra and S. Wright, "Interior-point methods," J. Comput. Appl. Math., vol. 124, no. 1/2, pp. 281-302, 2000. (Pubitemid 32023382)
25. T. Do, L. Gan, N. Nguyen, and T. Tran, "Sparsity adaptive matching pursuit algorithm for practical compressed sensing," in Proc. 42nd Asilomar Conf. Signals, Syst. Comput., 2008, pp. 581-587.
26. J. Tropp and S. Wright, "Computational methods for sparse solution of linear inverse problems," Proc. IEEE, vol. 98, no. 6, pp. 948-958, Jun. 2010.
27. S. Ji, Y. Xue, and L. Carin, "Bayesian compressive sensing," IEEE Trans. Signal Process., vol. 56, no. 6, pp. 2346-2356, Jun. 2008. (Pubitemid 351795888)
28. M. Tipping, "Sparse Bayesian learning and the relevance vector machine," Mach. Learn. Res., vol. 1, pp. 211-244, Sep. 2001. (Pubitemid 33687203)
29. A. Papoulis, S. Pillai, and S. Unnikrishna, Probability, Random Variables, and Stochastic Processes. New York: McGraw-Hill, 2002.
30. J. Bernado and A. Smith, Bayesian Theory. New York: Wiley, 1994.