Abstract: In surveillance videos of public places,the movement states of the crowds are various and complex,and it is difficult to analyze the movement state of the whole crowd through detecting or segmenting every individual.Therefore,it is an effective way to understand and analyze the movement state of the crowd by dividing the crowd into areas with basically the same movement state.Supervised crowd motion segmentation methods require pixel-level training sets with high labeling costs,and thus unsupervised clustering methods are more promising for crowd motion segmentation.However,since the local features describing crowd movements usually change gradually,leading to the unsupervised methods based on clustering algorithm need to choose different parameters for different crowd scenarios,it is difficult to adapt to a variety of different application scenarios.To this end,this paper proposes a motion contrast improvement-based crowd motion segmentation method.The method is an unsupervised model that first enhances the contrast of different motion states based on the distribution law of movement and noise in the motion field,and then combines the adaptive threshold segmentation algorithm and the marker watershed algorithm to extract the essentially consistent region for each motion state,avoiding the difficulty of parameter selection for unsupervised clustering methods.Based on the results of crowd motion segmentation,this paper presents an energy model to describe the stability of crowd movement.The energy model can enable early warning of abnormal crowd motion state by deducing the change process of the whole crowd motion state.Experiments are conducted on crowd motion segmentation in different types of complex crowd motion state scenes.Experimental results verify the effectiveness and segmentation accuracy of the motion contrast enhancement-based crowd motion segmentation method and the validity of the proposed energy model.

Key words: Crowd motion analysis, Motion contrast enhancement, Crowd motion segmentation, Energy model, Abnormal warning