Vision-Based Abnormal Vehicle Behavior Detection: A Survey

被引：0
|
作者：
Huang C. [1 ,3 ]
Hu Z. [2 ]
Xu Y. [1 ,3 ]
Wang Y. [3 ]
机构：
[1] Bio-Computing Research Center, College of Computer Science and Technology, Harbin Institute of Technology(Shenzhen), Shenzhen
[2] College of Mathematics and Statistics, Guangxi Normal University, Guilin
[3] Peng Cheng Laboratory, Shenzhen
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2020年 / 33卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Abnormal Vehicle Behavior Detection; Behavior Learning; Behavior Modeling; Deep Learning; Feature Extraction;
D O I：
10.16451/j.cnki.issn1003-6059.202003005
中图分类号：
学科分类号：
摘要：
Vision-based abnormal vehicle behavior detection can detect abnormal vehicle behaviors in the traffic surveillance video promptly and give an alarm. It plays an important role in improving the efficiency of traffic enforcement and traffic conditions and reducing traffic accident rate. Despite the progresses in abnormal vehicle behavior detection, there are still many challenges in practical application, such as lack of labeled data, uncertain anomaly, occlusion and poor real time capability. To make a clear understanding of abnormal vehicle behavior detection, the algorithms proposed in recent years are summarized. Firstly, the typical features representing vehicle behaviors are introduced, and the advantages and disadvantages of model learning methods of the algorithms are discussed from the perspectives of supervised and unsupervised learning. Then, the existing algorithms are categorized into model-based, reconstruction-based and deep neural network-based methods. Each category is introduced and analyzed. Finally, problems and prediction of the future of abnormal vehicle behavior detection are discussed. © 2020, Science Press. All right reserved.
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收藏
页码：234 / 248
页数：14
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