人体异常行为识别与检测方法综述
作者:
作者单位:

北方工业大学

作者简介:

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中图分类号:

TP391.4

基金项目:

北京市自然科学基金项目(4204096);国家自然科学基金项目(61903006);北京市长城学者培养计划项目(CIT&TCD 20190304);国家重点研发计划(2017YFC0821102, 2017YFC0822504);北方工业大学青年毓优项目;北方工业大学科研启动金


Review of recognition and detection methods of human abnormal behavior
Author:
Affiliation:

North China University of Technology

Fund Project:

Beijing Natural Science Foundation (4204096), National Natural Science Foundation of China (61903006), Beijing Municipal Great Wall Scholar Program (CIT&TCD 20190304), the National Key R&D Program of China under Grant (2017YFC0821102, 2017YFC0822504), Youth Yuyou Talent Project;Research Initial Foundation of North China University of Technology.

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    摘要:

    作为计算机视觉的重要分支,异常行为识别与检测技术已在智能安防、医疗监护、交通管控等领域获得了广泛应用.然而对异常行为的界定及判别方法与场景因素紧密相关, 针对不同应用场景特点, 适当选择特征提取及异常行为识别与检测方法, 进而保证预警准确率, 在实际应用中至关重要.基于此,本文对基于视频的人体异常行为识别与检测方法进行综述, 首先给出了人体异常行为的定义、特点及分类; 其次, 对特征提取方法进行了总结, 特征提取方法的选取及提取特征的好坏直接影响后续判别结果; 之后, 从异常行为识别和异常行为检测两个角度对异常行为判别方法进行了分析和讨论, 并给出了常用异常行为检测数据集及相关算法表现. 最后,对本领域未来研究方向提出了展望.

    Abstract:

    As an important branch of computer vision, abnormal behavior recognition and detection technology has been widely used in intelligent security, medical monitoring, traffic control and other fields. However, the definition and discrimination methods of abnormal behavior are closely related to the scene factors, and it is very important to appropriately choose the feature extraction as well as abnormal behavior recognition and detection methods according to the characteristics of different application scenarios, so as to improve the warning accuracy. So, this paper reviews the video based human abnormal behavior recognition and detection methods. Firstly, the definition, characteristics and classification of human abnormal behavior are given. Secondly, the feature extraction methods are summarized. The selection of feature extraction methods and the quality of extracted features directly affect the subsequent discrimination results. Then, the paper analyzes and discusses the abnormal behavior discrimination methods from two aspects: abnormal behavior recognition and abnormal behavior detection. The common abnormal behavior detection data sets and related algorithms" performance are also given. Finally, the future research directions of this field are prospected.

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历史
  • 收稿日期:2020-10-16
  • 最后修改日期:2021-08-11
  • 录用日期:2021-02-10
  • 在线发布日期: 2021-03-03
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