Support Vector Machine-Based Human Behavior Classification in Crowd through Projection and Star Skeletonization

Abstract

Problem statement: Detection of individual’s abnormal human behaviors in the crowd has become a critical problem because in the event of terror strikes. This study presented a real-time video surveillance system which classifies normal and abnormal behaviors in crowds. The aim of this research was to provide a system which can aid in monitoring crowded urban environments. Approach: The proposed behaviour classification was through projection which separated individuals and using star skeletonization the features like body posture and the cyclic motion cues were obtained. Using these cues the Support Vector Machine (SVM) classified the normal and abnormal behaviors of human. Results: Experimental results demonstrated the method proposed was robust and efficient in the classification of normal and abnormal human behaviors. A comparative study of classification accuracy between principal component analysis and Support Vector Machine (SVM) classification was also presented. Conclusion: The proposed method classified the behavior such as running people in a crowded environment, bending down movement while most are walking or standing, a person carrying a long bar and a person waving hand in the crowd is classified.