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Abnormal Crowd Behavior Detection Using Size-Adapted Spatio-Temporal Features
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  • Abnormal Crowd Behavior Detection Using Size-Adapted Spatio-Temporal Features
  • Abnormal Crowd Behavior Detection Using Size-Adapted Spatio-Temporal Features
저자명
Wang. Bo,Ye. Mao,Li. Xue,Zhao. Fengjuan
간행물명
International Journal of Control, Automation and Systems
권/호정보
2011년|9권 5호|pp.905-912 (8 pages)
발행정보
제어로봇시스템학회
파일정보
정기간행물|ENG|
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기타
이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
서지반출

기타언어초록

Abnormal crowd behavior detection is an important research issue in computer vision. However, complex real-life situations (e.g., severe occlusion, over-crowding, etc.) still challenge the effectiveness of previous algorithms. Recently, the methods based on spatio-temporal cuboid are popular in video analysis. To our knowledge, the spatio-temporal cuboid is always extracted randomly from a video sequence in the existing methods. The size of each cuboid and the total number of cuboids are determined empirically. The extracted features either contain the redundant information or lose a lot of important information which extremely affect the accuracy. In this paper, we propose an improved method. In our method, the spatio-temporal cuboid is no longer determined arbitrarily, but by the information contained in the video sequence. The spatio-temporal cuboid is extracted from video sequence with adaptive size. The total number of cuboids and the extracting positions can be determined automatically. Moreover, to compute the similarity between two spatio-temporal cuboids with different sizes, we design a novel data structure of codebook which is constructed as a set of two-level trees. The experiment results show that the detection rates of false positive and false negative are significantly reduced.