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Traffic anomaly detection based on image descriptor in videos

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Abstract

The huge and ever growing volume of traffic video poses a compelling demand for efficient automatic detection of traffic anomaly. In this paper, a new traffic anomaly detection algorithm is introduced. It firstly divides a traffic video into several video cubes in temporal domain, and each video cube is divided into video blocks in spatial domain. Each image block of a video block is described using the local invariant features and the visual codebook approach. Based on the descriptor of the image block, we count the category number of the block (CNB) of a video block. Then, a Gaussian distribution model for estimating the probability of normal traffic with respect to the CNB is learned. The learned Gaussian distribution model is then used to detect the traffic anomaly from the test traffic video. Eventually, the results of all video blocks are fused to achieve the final decision. Experimental results show that the proposed algorithm performs better than two existing algorithms on both the intersection traffic videos and main road traffic videos.

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Acknowledgments

This work was supported by National Natural Science Funds of China (Nos. 61401286 and 61372007), Guangdong Provincial Project of Transportation Science and Technology (No. 2012-02-084), Natural Science Funds of Guangdong Province (Nos. S2012010009885 and S2013010012966), Projects of innovative science and technology, Department of Education, Guangdong Province (No.2013KJCX0012), and Natural Science Foundation of SZU (No. 201415).

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Authors and Affiliations

  1. School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, 510640, China

    Yanshan Li & Weiming Liu

  2. School of Electronic and Information Engineering, South China University of Technology, Guangzhou, 510640, China

    Yanshan Li & Qinghua Huang

  3. 3. Information Engineering College, Shenzhen University, Shenzhen, 518060, China

    Yanshan Li

Authors
  1. Yanshan Li
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  2. Weiming Liu
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  3. Qinghua Huang
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Correspondence to Qinghua Huang.

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Li, Y., Liu, W. & Huang, Q. Traffic anomaly detection based on image descriptor in videos. Multimed Tools Appl 75, 2487–2505 (2016). https://doi.org/10.1007/s11042-015-2637-y

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  • DOI: https://doi.org/10.1007/s11042-015-2637-y

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