Open Access

ARTICLE

SOINN-Based Abnormal Trajectory Detection for Efficient Video Condensation

Chin-Shyurng Fahn¹, Chang-Yi Kao^2,*, 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:

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

Computer Systems Science and Engineering 2022, 42(2), 451-463. https://doi.org/10.32604/csse.2022.022368

Received 05 August 2021; Accepted 10 September 2021; Issue published 04 January 2022

Abstract

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.

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Keywords

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