SOINN-Based Abnormal Trajectory Detection for Efficient Video Condensation

作     者：Chin-Shyurng Fahn Chang-Yi Kao Meng-Luen Wu Hao-En Chueh 

作者机构：National Taiwan University of Science and TechnologyTaipei City106335Taiwan Soochow UniversityTaipei City100Taiwan Tamkang UniversityNew Taipei City251301Taiwan Chung Yuan Christian UniversityTaoyuan City32023Taiwan 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2022年第42卷第8期

页      面：451-463页

核心收录：

学科分类：0817[工学-化学工程与技术] 08[工学] 0837[工学-安全科学与工程] 0703[理学-化学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Surveillance systems video condensation SOINN moving trajectory abnormal detection 

摘      要：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 *** 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 *** goal is to improve the condensation rate to reduce more storage size,and increase the accuracy in abnormal *** 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 *** 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.