吉林大学学报(工学版) ›› 2021, Vol. 51 ›› Issue (5): 1823-1829.doi: 10.13229/j.cnki.jdxbgxb20200441

• 计算机科学与技术 • 上一篇    

基于有效异常样本构造的视频异常检测算法

侯春萍(),赵春月,王致芃,田海瑞   

  1. 天津大学 电气自动化与信息工程学院,天津 300072
  • 收稿日期:2020-06-19 出版日期:2021-09-01 发布日期:2021-09-16
  • 作者简介:侯春萍(1957-),女,教授,博士生导师.研究方向:图像质量评价.E-mail:hcp@tju.edu.cn
  • 基金资助:
    国际合作与交流NFSC项目(61520106002);国家自然科学基金项目(61731003)

Video anomaly detection algorithm based on effective anomaly sample construction

Chun-ping HOU(),Chun-yue ZHAO,Zhi-peng WANG,Hai-rui TIAN   

  1. School of Electrical Automation and Information Engineering,Tianjin University,Tianjin 300072,China
  • Received:2020-06-19 Online:2021-09-01 Published:2021-09-16

摘要:

为改善大多数异常检测算法仅通过正常样本训练模型,缺乏异常样本,将会造成一定程度的误判问题,提出了一种基于有效异常样本构造的异常检测算法。通过K-means聚类算法得到代表不同类型正常事件的聚类簇,然后,基于异常事件的时序关系构造异常样本,再结合本文构造的异常样本,利用二分类支持向量机算法训练分类器,将检测任务转化为分类任务,从而提高检测准确率。本文在经典数据集(Avenue数据集)上进行了算法有效性验证,发现本文算法的检测准确度优于一些领域内的先进算法。因此,充分利用视频的时序关系进行异常样本的构造能有效提高异常检测的有效性。

关键词: 通信与信息系统, 视频处理, 异常检测, 支持向量机

Abstract:

At present, most anomaly detection algorithms only train models through normal samples, but the lack of abnormal sample may cause misjudgment. This paper proposes an anomaly detection algorithm based on effective anomaly sample construction. The clustering clusters representing different types of normal events are obtained by K-means clustering algorithm. Then, the abnormal samples are constructed based on the temporal relationship of abnormal events. Combined with the abnormal samples constructed in this paper, the classifier is trained by binary classification support vector machine algorithm to transform the detection task into classification task, thereby improve the detection accuracy. The effectiveness of the algorithm is verified on the classic dataset(the Avenue dataset), and it is found that the detection accuracy of this algorithm is better than some advanced algorithms. Therefore, making full use of the timing relationship of video to construct abnormal samples can effectively improve the effectiveness of abnormal detection.

Key words: communication and information system, video processing, abnormality detection, support vector machines

中图分类号: 

  • TN911.73

图1

算法流程图"

图2

算法原理图"

图3

构造假异常样本数据集"

图4

训练支持向量机分类器"

表1

评估异常样本特征数量变化对异常检测的影响"

dAUC
0.90.786
1.00.790
1.10.794
1.20.798
1.30.795
1.40.794

图5

单分类支持向量机与二分类支持向量机训练模型结果对比"

图6

视频中的异常行为"

表2

当包含或者不含异常样本时检测准确率的变化"

算法AUC
文献[150.702
文献[190.783
文献[80.788
K-means+one-class SVM0.576
本文0.798
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