Abnormal behaviors detection using particle motion model

Yutao Chen; Hong Zhang; Feiyang Cheng; Ding Yuan; Yuhu You

doi:10.1117/12.2179331

4 March 2015 Abnormal behaviors detection using particle motion model

Yutao Chen, Hong Zhang, Feiyang Cheng, Ding Yuan, Yuhu You

Proceedings Volume 9443, Sixth International Conference on Graphic and Image Processing (ICGIP 2014); 94430J (2015) https://doi.org/10.1117/12.2179331
Event: Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 2014, Beijing, China

Abstract

Human abnormal behaviors detection is one of the most challenging tasks in the video surveillance for the public security control. Interaction Energy Potential model is an effective and competitive method published recently to detect abnormal behaviors, but their model of abnormal behaviors is not accurate enough, so it has some limitations. In order to solve this problem, we propose a novel Particle Motion model. Firstly, we extract the foreground to improve the accuracy of interest points detection since the complex background usually degrade the effectiveness of interest points detection largely. Secondly, we detect the interest points using the graphics features. Here, the movement of each human target can be represented by the movements of detected interest points of the target. Then, we track these interest points in videos to record their positions and velocities. In this way, the velocity angles, position angles and distance between each two points can be calculated. Finally, we proposed a Particle Motion model to calculate the eigenvalue of each frame. An adaptive threshold method is proposed to detect abnormal behaviors. Experimental results on the BEHAVE dataset and online videos show that our method could detect fight and robbery events effectively and has a promising performance.

Citation Download Citation

Yutao Chen, Hong Zhang, Feiyang Cheng, Ding Yuan, and Yuhu You "Abnormal behaviors detection using particle motion model", Proc. SPIE 9443, Sixth International Conference on Graphic and Image Processing (ICGIP 2014), 94430J (4 March 2015); https://doi.org/10.1117/12.2179331

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KEYWORDS

Motion models

Particles

Video surveillance

Video

Motion detection

Target detection

Chaos

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