Abnormal Behavior Detection Based on Dynamic Pedestrian Centroid Model: Case Study on U-Turn and Fall-Down

With the increasing number of video surveillance cameras in public buildings, it has become challenging, yet significant to detect abnormal pedestrian behaviors in crowd management, to prevent crowd accidents. Although current advancements in human action recognition based on computer vision can help detect abnormal behaviors after their incidence, majority of them lack the ability to detect potential characteristics prior to the occurrence of real abnormal behaviors. Hence, in this study, we addressed this issue by proposing a novel dynamic centroid model (DCM) of a human body, and rebuilding pedestrian joint sub-segments from human skeleton key nodes obtained in camera images. We built a weighted centroid-combined force model based on Newton's second law, considering acceleration, mass inertial of human body sub-segments, and internal constraints. Thereafter, pedestrian kinematic and dynamic parameters were analyzed, such as speed, trajectory, force. Furthermore, abnormal behavior detection criteria were constructed for typical abnormal-behavior cases: U-turn and fall-down. Comparative experiments between the proposed DCM and the state-of-the-art methods were conducted. The experimental results showed that the model was capable of detecting abnormal behaviors, with mean values of lead time of 277 ms in U-turn behavior, and 562 ms in fall-down behavior, prior to the captured occurrence of these two abnormal behaviors. Finally, a de-occlusion algorithm was designed and jointly used with DCM, validated by a fall-down detecting experiment including partial occlusion. Therefore, this study holds significant value for the prevention of abnormal pedestrian behaviors in public places.