基于监控视频的车辆异常行为检测方法综述
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北京科技大学自动化学院

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中图分类号:

TG 142.71

基金项目:

北京科技大学顺德研究生院科技创新专项资金 ( No. BK19CE017 ) [项目名称:工业过程多模态数据挖掘与异常监测决策方法研究]


Surveillance Video-Based Abnormal Vehicle Behavior Detection: A Survey
Author:
Affiliation:

School of AutomationSandSElectricalSEngineering,University of Science and Technology Beijing

Fund Project:

Scientific and Technological Innovation Foundation of Shunde Graduate School, USTB ( No. BK19CE017 )[Research on the multi-mode data mining and abnormal monitoring decision-making method in industrial process]

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    摘要:

    交通视频中的异常检测是一项基本的计算机视觉任务,因其在智能交通系统中的重要性而受到越来越广泛的关注。面临着如交通场景的复杂性,缺乏数据、异常行为定义的不确切性、交通流的密集混乱,影响实时流量馈送的视频质量等问题,车辆异常检测仍然是一个具有挑战性的问题。本文归纳总结近年来提出的基于监控视频的车辆异常行为检测算法。首先,介绍当前算法中使用的车辆检测框架和跟踪框架。然后从异常特征提取和行为学习建模方法两方面对车辆异常行为检测方法进行介绍和分析。最后介绍了常用数据集,并进行总结,展望了未来的发展方向。

    Abstract:

    Anomaly detection in traffic video is a basic computer vision task, which has been paid more and more attention due to its importance in the intelligent transportation systems. Faced with such problems as the complexity of traffic scene, lack of data, inaccurate definition of abnormal behavior, dense and chaotic traffic flow affecting the video quality of real-time traffic feeding, it is still a challenging problem. The vehicle abnormal behavior detection algorithms based on surveillance video proposed in recent years are summarized in this paper. Firstly, the vehicle detection framework and tracking framework used in current algorithms are introduced. Then the vehicle abnormal behavior detection methods are introduced and analyzed from two aspects of abnormal feature extraction and behavior learning modeling methods. Finally, commonly used datasets are presented, and future directions are discussed.

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引用本文

梁 鹏,刘 丽. 基于监控视频的车辆异常行为检测方法综述[J]. 科学技术与工程, , ():

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  • 收稿日期:2021-04-19
  • 最后修改日期:2021-09-17
  • 录用日期:2021-09-28
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