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SOINN-Based Abnormal Trajectory Detection for Efficient Video Condensation

Chin-Shyurng Fahn1, Chang-Yi Kao2,*, Meng-Luen Wu3, Hao-En Chueh4

1 National Taiwan University of Science and Technology, Taipei City, 106335, Taiwan
2 Soochow University, Taipei City, 100, Taiwan
3 Tamkang University, New Taipei City, 251301, Taiwan
4 Chung Yuan Christian University, Taoyuan City, 32023, Taiwan

* Corresponding Author: Chang-Yi Kao. Email: email

(This article belongs to the Special Issue: Advances in Computational Intelligence and its Applications)

Computer Systems Science and Engineering 2022, 42(2), 451-463.


With the evolution of video surveillance systems, the requirement of video storage grows rapidly; in addition, safe guards and forensic officers spend a great deal of time observing surveillance videos to find abnormal events. As most of the scene in the surveillance video are redundant and contains no information needs attention, we propose a video condensation method to summarize the abnormal events in the video by rearranging the moving trajectory and sort them by the degree of anomaly. Our goal is to improve the condensation rate to reduce more storage size, and increase the accuracy in abnormal detection. As the trajectory feature is the key to both goals, in this paper, a new method for feature extraction of moving object trajectory is proposed, and we use the SOINN (Self-Organizing Incremental Neural Network) method to accomplish a high accuracy abnormal detection. In the results, our method is able to shirk the video size to 10% storage size of the original video, and achieves 95% accuracy of abnormal event detection, which shows our method is useful and applicable to the surveillance industry.


Cite This Article

APA Style
Fahn, C., Kao, C., Wu, M., Chueh, H. (2022). Soinn-based abnormal trajectory detection for efficient video condensation. Computer Systems Science and Engineering, 42(2), 451-463.
Vancouver Style
Fahn C, Kao C, Wu M, Chueh H. Soinn-based abnormal trajectory detection for efficient video condensation. Comput Syst Sci Eng. 2022;42(2):451-463
IEEE Style
C. Fahn, C. Kao, M. Wu, and H. Chueh "SOINN-Based Abnormal Trajectory Detection for Efficient Video Condensation," Comput. Syst. Sci. Eng., vol. 42, no. 2, pp. 451-463. 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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