Crime scene classification from skeletal trajectory analysis in surveillance settings

Video anomaly analysis is a core task to the field of computer vision, particularly for crime detection in surveillance footage. In this work, we address the task of human-related crime classification using skeletal joint trajectories extracted from surveillance video frames. First, we emphasize the need to enhance the ground truth labels for the Human-Related Crime dataset (HR-Crime) and propose supervised and unsupervised methodologies to generate trajectory-level labels. Next, based on the trajectory-level labels, we introduce a trajectory-based crime classification framework, evaluating different architectures and feature fusion strategies for representing human trajectories. Our experiments validate the approach and open avenues for future research on this topic.