Recognising pedestrian behaviour using a multi-channel spatiotemporal fusion network

Abstract

To protect public safety, video abnormal behaviour detection is essential. The low accuracy of identifying abnormal behaviour in pedestrians is the focus of this study. To address this, an abnormal target identification method based on multi-feature fusion of trajectory skeleton is proposed. The process begins with defining the type of abnormal behaviour in accordance with the environmental requirements. Next, pedestrian identification is carried out in the designated area, the pedestrian is tracked to determine its movement trajectory, and the image coordinates of the corresponding relevant nodes are calculated by analysing the human posture. Lastly, the track and skeleton features are integrated to classify the normal and abnormal behaviours, and the identification of abnormal target behaviours is accomplished. Test results on the behavioural analysis database indicate that this algorithm's accuracy is 87.08%, which is higher than the single-feature identification method's detection efficiency of abnormal behaviours.