基于视觉的车辆异常行为检测综述

Vision-Based Abnormal Vehicle Behavior Detection:A Survey

基于视觉的车辆异常行为检测能够快速检测交通监控视频中的车辆异常行为并报警,在提升交通执法效率,改善城市交通状况和减少交通事故率等方面具有重要作用.当前基于视觉的车辆异常行为检测已取得较大进步,但在实际应用中仍面临如缺乏数据、异常定义的不确切性、遮挡和实时性较差等问题.文中归纳总结近年来提出的基于视觉的车辆异常行为检测算法.首先,介绍当前算法中典型的行为表示特征,从监督学习和非监督学习两方面讨论现有车辆行为学习方法的优缺点.然后,根据行为建模方法将车辆异常行为检测算法分为基于模型的方法、基于重建的方法和深度学习方法,介绍和分析每类方法.最后,讨论当前算法存在的问题,并展望未来的改进方向.

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.