Motion Influence Map for Unusual Human Activity Detection and Localization in Crowded Scenes

• Published in: IEEE transactions on circuits and systems for video technology Vol. 25; no. 10; pp. 1612 - 1623
• Main Authors: Lee, Dong-Gyu, Suk, Heung-Il, Park, Sung-Kee, Lee, Seong-Whan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bicycles; crowded scenes; Feature extraction; Force; Indexes; Legged locomotion; motion influence map; Surveillance; Unusual activity detection; Vectors; vision-based surveillance; motion influence map; vision-based surveillance; Crowded scenes; unusual activity detection
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2015.2395752

In this paper, we propose a novel method for unusual human activity detection in crowded scenes. Specifically, rather than detecting or segmenting humans, we devised an efficient method, called a motion influence map, for representing human activities. The key feature of the proposed motion influence map is that it effectively reflects the motion characteristics of the movement speed, movement direction, and size of the objects or subjects and their interactions within a frame sequence. Using the proposed motion influence map, we further developed a general framework in which we can detect both global and local unusual activities. Furthermore, thanks to the representational power of the proposed motion influence map, we can localize unusual activities in a simple manner. In our experiments on three public datasets, we compared the performances of the proposed method with that of other state-of-the-art methods and showed that the proposed method outperforms these competing methods.