Multiple Vehicle Detection and Tracking in Highway Traffic Surveillance Video Based on SIFT Feature Matching Mu, Kenan; Hui, Fei; Zhao, Xiangmo;
This paper presents a complete method for vehicle detection and tracking in a fixed setting based on computer vision. Vehicle detection is performed based on Scale Invariant Feature Transform (SIFT) feature matching. With SIFT feature detection and matching, the geometrical relations between the two images is estimated. Then, the previous image is aligned with the current image so that moving vehicles can be detected by analyzing the difference image of the two aligned images. Vehicle tracking is also performed based on SIFT feature matching. For the decreasing of time consumption and maintaining higher tracking accuracy, the detected candidate vehicle in the current image is matched with the vehicle sample in the tracking sample set, which contains all of the detected vehicles in previous images. Most remarkably, the management of vehicle entries and exits is realized based on SIFT feature matching with an efficient update mechanism of the tracking sample set. This entire method is proposed for highway traffic environment where there are no non-automotive vehicles or pedestrians, as these would interfere with the results.
A. Jazayeri, H. Cai, J. Y. Zheng, and M. Tuceryan, "Vehicle detection and tracking in car video based on motion model," IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 2, pp. 583-595, 2011.
D. H. Cho, M. N. Ali, S. J. Chun, and S. L. Lee, "vehicle association and tracking in image sequences using feature-based similarity comparison," Applied Mechanics and Materials, vol. 536-537, pp. 176-179, 2014.
A. Broggi, A. Cappalunga, S. Cattani, and P. Zani, "Lateral vehicles detection using monocular high resolution cameras on TerraMax," in Proceedings of 2008 IEEE Intelligent Vehicles Symposium, Eindhoven, The Netherlands, 2008, pp. 1143-1148.
S. S. Teoh and T. Braunl, "Symmetry-based monocular vehicle detection system," Machine Vision and Applications, vol. 23. No. 5, pp. 831-842, 2012.
A. Kanitkar, B. Bharti, and U. N. Hivarkar, "Vision based preceding vehicle detection using self shadows and structural edge features," in Proceedings of 2011 International Conference on Image Information Processing (ICIIP), Himachal Pradesh, India, 2011, pp. 1-6.
I. Szottka and M. Butenuth, "Advanced particle filtering for airborne vehicle tracking in urban areas," IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 3, pp. 686-690, 2014.
W. C. Chang and C. W. Cho, "Real-time side vehicle tracking using parts-based boosting," in Proceedings of IEEE International Conference on Systems, Man and Cybernetics (SMC2008), Singapore, 2008, pp. 3370-3375.
A. Ess, B. Leibe, K. Schindler, and L. Van Gool, "Robust multiperson tracking from a mobile platform," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 10, pp. 1831-1846, 2009.
Z. Zivkovic, A. T. Cemgil, and B. Krose, "Approximate Bayesian methods for kernel-based object tracking," Computer Vision and Image Understanding, vol. 113, no. 6, pp. 743-749, 2009.
S. Yang, J. Xu, Y. Chen, and M. Wang, "On-road vehicle tracking using keypoint-based representation and online co-training," Multimedia Tools and Applications, vol. 72, no. 2, pp. 1561-1583, 2014.
T. Gao, G. Li, S. Lian, and J. Zhang, "Tracking video objects with feature points based particle filtering," Multimedia Tools and Applications, vol. 58, no. 1, pp. 1-21, 2012.
J. Arrospide, L. Salgado, and M. Nieto, "Vehicle detection and tracking using homography-based plane rectification and particle filtering," in Proceedings of 2010 IEEE Intelligent Vehicles Symposium (IV), San Diego, CA, 2010, pp. 150-155.
X. Wu, Q. Zhao, and W. Bu, "A SIFT-based contactless palmprint verification approach using iterative RANSAC and local palmprint descriptors," Pattern Recognition, vol. 47, no. 10, pp. 3314-3326, 2014.
J. X. Tan, S. D. Li, and R. H. Yang, "Comparative study on tree structure-based KNN methods of ICP matching algorithm," Science of Surveying and Mapping, vol. 39, no. 4, pp. 152-155, 2014.
M. Li, "Research on object tracking algorithm based on SIFT feature-points Matching," M.S. thesis, Hefei University of Technology, China, 2011.