A detection method of individual fare evasion behaviours on metros based on skeleton sequence and time series.

Fare evasion behaviours in metro stations happen all the time with the continuous improvement of the metro network, leading to likely mass panic and unhealthy effect on the metro operation. This paper proposes an approach of detecting individual fare evasion behaviours. The method first estimates human pose by the algorithm of OpenPose to obtain human skeleton information and conducts multi-target tracking by the algorithm of Pose Flow to obtain human skeleton sequence. It then extracts features from the human pose, followed by the adoption of the relative distance, the angle and Random Forests to establish a fare evasion behaviour detection model. Since missing problems and false detections occur in detections in a single frame, time series are used to increase the accuracy. The output of the time series curve is processed by One-Hot Coding, Kalman Filtering and optimum threshold to obtain the final prediction of human status. Extensive experiments are conducted, and results show that our method can effectively recognize the human status and detect individual fare evasion behaviours, including jumps and squats. [ABSTRACT FROM AUTHOR]