SOINN-Based Abnormal Trajectory Detection for Efficient Video Condensation
學年 110
學期 1
出版（發表）日期 2022-01-04
作品名稱 SOINN-Based Abnormal Trajectory Detection for Efficient Video Condensation
作品名稱（其他語言）
著者 Chin-Shyurng Fahn; Chang-Yi Kao; Meng-Luen Wu; Hao-En Chueh
單位
出版者
著錄名稱、卷期、頁數 Computer Systems Science and Engineering 42(2), p.451-463
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 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.
關鍵字 Surveillance systems;video condensation;SOINN;moving trajectory;abnormal detection
語言 en_US
ISSN 2730-7794
期刊性質 國外
收錄於 SCI
產學合作
通訊作者
審稿制度 否
國別 CHE
公開徵稿
出版型式 ,電子版
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機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/121881 )

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