Video anomaly detection algorithm combining global and local video representation

被引：0
|
作者：
Hu Z. [1 ,2 ]
Zhao M. [1 ]
Xin B. [1 ]
机构：
[1] School of Information Science and Engineering, Yanshan University, Qinhuangdao
[2] Key Laboratory of Information Transmission and Signal Processing of Hebei Province, Yanshan University, Qinhuangdao
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2020年 / 33卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Abnormal Behavior Detection; Local Texture Motion Feature; Pattern Recognition; Spatiotemporal Motion Feature; Support Vector Data Description Model(SVDD);
D O I：
10.16451/j.cnki.issn1003-6059.202002005
中图分类号：
学科分类号：
摘要：
Aiming at the problem of video anomaly detection, a video anomaly detection algorithm combining global and local video representation is proposed. Firstly, the input video continuous multi-frames are divided into video blocks. The video blocks are divided into non-overlapping space-time cubes according to the spatial position. The global spatiotemporal grid position support vector data description(SVDD) model based on spatial position is constructed using the space-time cubes motion features. Then, the local texture motion features are extracted for the moving targets of videos. SVDD algorithm is utilized to obtain the hypersphere boundary around the target features, and the normal behavior model of the moving targets is constructed. Finally, the two parts are combined to conduct more comprehensive detection. Experiments on public datasets verify the effectiveness of the proposed algorithm. © 2020, Science Press. All right reserved.
引用
收藏
页码：133 / 140
页数：7
相关论文
共 15 条
  • [1] Kim J., Grauman K., Observe Locally, Infer Globally: A Space-Time MRF for Detecting Abnormal Activities with Incremental Updates, Proc of the IEEE Conference on Computer Vision and Pa-ttern Recognition, pp. 2921-2928, (2009)
  • [2] Lee D.G., Suk H.I., Lee S.W., Crowd Behavior Representation Using Motion Influence Matrix for Anomaly Detection, Proc of the 2nd IAPR Asian Conference on Pattern Recognition, pp. 110-114, (2013)
  • [3] Chen T.Y., Hou C.P., Wang Z.P., Et al., Anomaly Detection in Crowded Scenes Using Motion Energy Model, Multimedia Tools and Applications, 77, 11, pp. 14137-14152, (2018)
  • [4] Mahadevan V., Li W.X., Bhalodia V., Et al., Anomaly Detection in Crowded Scenes, Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1975-1981, (2010)
  • [5] Shen M.Y., Jiang X.H., Sun T.F., Anomaly Detection Based on Nearest Neighbor Search with Locality-Sensitive B-tree, Neurocomputing, 289, pp. 55-67, (2018)
  • [6] Zhou S.F., Shen W., Zeng D., Et al., Spatial-Temporal Convolutional Neural Networks for Anomaly Detection and Localization in Crowded Scenes, Signal Processing(Image Communication), 47, pp. 358-368, (2016)
  • [7] Sabokrou M., Fayyaz M., Fathy M., Et al., Deep-Anomaly: Fully Convolutional Neural Network for Fast Anomaly Detection in Crowded Scenes, Computer Vision and Image Understanding, 172, pp. 88-97, (2018)
  • [8] Zhou P.P., Ding Q.H., Luo H.B., Et al., Anomaly Detection and Location in Crowded Surveillance Videos, Acta Optica Sinica, 38, 8, pp. 97-105, (2018)
  • [9] Wang S.Q., Zhu E., Yin J.P., Et al., Video Anomaly Detection and Localization by Local Motion Based Joint Video Representation and OCELM, Neurocomputing, 277, pp. 161-175, (2018)
  • [10] Liu P., Tao Y., Zhao W., Et al., Abnormal Crowd Motion Detection Using Double Sparse Representation, Neurocomputing, 269, pp. 3-12, (2017)
12