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Abnormal Crowd Motion Detection with Hidden Conditional Random Fields Model
Dongping Zhang,Kaihang Xu,Yafei Lu,Chen Pan,Huailiang Peng 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.10
Crowd motion analysis in public places is an important research subject in the monitoring field. This paper proposes an approach for detecting abnormal crowd motion using Hidden Conditional Random Fields Model (HCRF). This approach derives variations of motion patterns from direction distribution of the crowd motion obtained by the optical flow and these variations are encoded with HCRF to allow for the detection of abnormal crowd motion. Modeling the temporal neighborhood relations in a video sequence based on HCRF can incorporate hidden states and label the video depending on long range observations. The experimental results show that this proposed algorithm can achieve better results than HMM and CRF.
Abnormal Crowd Motion Behaviour Detection based on SIFT Flow
Dongping Zhang,Kaihang Xu,Huailiang Peng,Ye Shen 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.1
This paper focuses on the detection of the abnormal motion behaviour recognition of the crowd, and proposes an innovation method which is consist of three steps, i.e. SIFT flow + weighted orientation histogram + Hidden Markov Model(HMM). Analogous to optical flow, which is used to get the motion information of the pixels from two adjacent frames, SIFT flow is of higher precision. Next, we build up a a weighted orientation histogram as a statistical measurement for the SIFT flow features from the first step. Finally, the derived histogram is taken as the input for HMM in preparation for the detection of abnormal crowd motion. Experimental results show that compared to the existing method, our proposed one can detect the abnormal motion behaviour more effectively.