Multiple Vehicle Detection and Tracking in Highway Traffic Surveillance Video Based on SIFT Feature Matching

Kenan Mu, Fei Hui and Xiangmo Zhao
Volume: 12, No: 2, Page: 183 ~ 195, Year: 2016
10.3745/JIPS.02.0040
Keywords: Computer Vision, Highway Traffic Surveillance, SIFT Feature Matching, Vehicle Detection, Vehicle Tracking
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Abstract
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.

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Cite this article
IEEE Style
Kenan Mu, Fei Hui, and Xiangmo Zhao, "Multiple Vehicle Detection and Tracking in Highway Traffic Surveillance Video Based on SIFT Feature Matching," Journal of Information Processing Systems, vol. 12, no. 2, pp. 183~195, 2016. DOI: 10.3745/JIPS.02.0040.

ACM Style
Kenan Mu, Fei Hui, and Xiangmo Zhao, "Multiple Vehicle Detection and Tracking in Highway Traffic Surveillance Video Based on SIFT Feature Matching," Journal of Information Processing Systems, 12, 2, (2016), 183~195. DOI: 10.3745/JIPS.02.0040.