Highway traffic congestion classification using holistic properties

Proceedings of the IASTED International Conference on Signal Processing, Pattern Recognition and Applications, SPPRA 2013

Volumn , Issue , 2013, Pages 458-465

  Sobral, Andrews ^a   Oliveira, Luciano ^a   Schnitman, Leizer ^a   De Souza, Felippe ^b  

^a FEDERAL UNIVERSITY OF BAHIA   (Brazil)

^b UNIVERSITY OF BEIRA INTERIOR   (Portugal)

Author keywords

Neural network applications; Object recognition and motion; Pattern recognition

Indexed keywords

BACKGROUND SUBTRACTION; CROWD DENSITY; DATA SET; HIGHWAY TRAFFIC; HIGHWAY TRAFFIC CONGESTION; NEURAL NETWORK APPLICATION;

HIGHWAY TRAFFIC CONTROL; NEURAL NETWORKS; OBJECT RECOGNITION; PATTERN RECOGNITION; SIGNAL PROCESSING; TRAFFIC CONGESTION;

CLASSIFICATION (OF INFORMATION);

EID: 84876564392     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.2316/P.2013.798-105     Document Type: Conference Paper

References (30)

1. A. Albiol, L. Sanchis, A. Albiol, and J. Mossi. Detection of parked vehicles using spatiotemporal maps. IEEE Transactions on Intelligent Transportation Systems (ITS), 12(4):1277-1291, dec. 2011.
2. N. Buch, J. Orwell, and S. Velastin. Detection and classification of vehicles for urban traffic scenes. In 5th International Conference on Visual Information Engineering (VIE), pages 182-187, aug 2008.
3. N. Buch, S. Velastin, and J. Orwell. A review of computer vision techniques for the analysis of urban traffic. IEEE Transactions on Intelligent Transportation Systems (ITS), 12(3):920-939, sept. 2011.
4. A. Chan and N. Vasconcelos. Classification and retrieval of traffic video using auto-regressive stochastic processes. In IEEE Intelligent Vehicles Symposium (IVS), pages 771-776, june 2005.
5. K. G. Derpanis and R. P. Wildes. Classification of traffic video based on a spatiotemporal orientation analysis. In Proceedings of the 2011 IEEE Workshop on Applications of Computer Vision, WACV'11, pages 606-613, 2011.
6. F. El Baf, T. Bouwmans, and B. Vachon. Fuzzy integral for moving object detection. In IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pages 1729-1736, june 2008.
7. S. Gupte, O. Masoud, R. Martin, and N. Papanikolopoulos. Detection and classification of vehicles. IEEE Transactions on Intelligent Transportation Systems (ITS), 3(1):37-47, mar 2002.
8. W. He and Z. Liu. Motion pattern analysis in crowded scenes by using density based clustering. In 9th International Conference on Fuzzy Systems and Knowledge Discovery, pages 1855-1858, may 2012.
9. M. Hofmann, P. Tiefenbacher, and G. Rigoll. Background segmentation with feedback: The pixel-based adaptive segmenter. june 2012.
10. W.-L. Hsu, K.-F. Lin, and C.-L. Tsai. Crowd density estimation based on frequency analysis. In Seventh International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), pages 348-351, oct. 2011.
11. J. Jacques Junior, S. Musse, and C. Jung. Crowd analysis using computer vision techniques. IEEE Signal Processing Magazine, 27(5):66-77, sept. 2010.
12. P.-M. Jodoin, V. Saligrama, and J. Konrad. Behavior subtraction. IEEE Transactions on Image Processing, 21(9):4244-4255, sept. 2012.
13. S. JunFang, B. ANing, and X. Ru. A reliable counting vehicles method in traffic flow monitoring. In 4th International Congress on Image and Signal Processing (CISP), Volume 1, pages 522-524, oct. 2011.
14. V. Kastrinaki, M. Zervakis, and K. Kalaitzakis. A survey of video processing techniques for traffic applications. Image and Vision Computing, 21(4):359-381, 2003.
15. Y. Lecun, L. Bottou, G. B. Orr, and K. R. Müller. Efficient backprop. In G. Orr and K. Müller, editors, Neural Networks - Tricks of the Trade, Volume 1524 of Lecture Notes in Computer Science, pages 5-50. Springer Verlag, 1998.
16. J. Lee and A. Bovik. Estimation and analysis of urban traffic flow. In 16th IEEE International Conference on Image Processing, pages 1157-1160, nov 2009.
17. B. D. Lucas and T. Kanade. An iterative image registration technique with an application to stereo vision. In Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2, pages 674-679, 1981.
18. L. Maddalena and A. Petrosino. A self-organizing approach to background subtraction for visual surveillance applications. IEEE Transactions on Image Processing, 17(7):1168-1177, july 2008.
19. L. Maddalena and A. Petrosino. A fuzzy spatial coherence-based approach to background/foreground separation for moving object detection. Neural Comput. Appl., 19(2):179-186, March 2010.
20. N. C. Mithun, N. U. Rashid, and S. M. M. Rahman. Detection and classification of vehicles from video using multiple time-spatial images. IEEE Transactions on Intelligent Transportation Systems (ITS), PP(99):1-11, 2012.
21. F. Porikli and X. Li. Traffic congestion estimation using hmm models without vehicle tracking. In IEEE Intelligent Vehicles Symposium (IVS), pages 188-193, june 2004.
22. C. Pornpanomchai and K. Kongkittisan. Vehicle speed detection system. In IEEE International Conference on Signal and Image Processing Applications (ICSIPA), pages 135-139, nov. 2009.
23. M. Riedmiller and H. Braun. A direct adaptive method for faster backpropagation learning: The rprop algorithm. In IEEE International Conference on Neural Networks, pages 586-591, 1993.
24. F. Santoro, S. Pedro, Z.-H. Tan, and T. B. Moeslund. Crowd analysis by using optical flow and density based clustering. Proceedings of the European Signal Processing Conference, 18:269-273, 2010.
25. S. Saxena, F. Brémond, M. Thonnat, and R. Ma. Crowd behavior recognition for video surveillance. In Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS '08, pages 970-981, Berlin, Heidelberg, 2008. Springer-Verlag.
26. J. Shi and C. Tomasi. Good features to track. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pages 593-600, jun 1994.
27. M. Valera and S. Velastin. Intelligent distributed surveillance systems: a review. IEEE Proceedings on Vision, Image and Signal Processing, 152(2):192-204, april 2005.
28. J. Yao and J. Odobez. Multi-layer background subtraction based on color and texture. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1-8, june 2007.
29. J. yves Bouguet. Pyramidal implementation of the lucas kanade feature tracker. Intel Corporation, Microprocessor Research Labs, 2000.
30. B. Zhan, D. N. Monekosso, P. Remagnino, S. A. Ve-lastin, and L.-Q. Xu. Crowd analysis: a survey. Machine Vision Applications, 19(5-6):345-357, 2008.