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Video anomaly analysis is a core task actively pursued in the field ofcomputer vision, with applications extending to real-world crime detection insurveillance footage. In this work, we address the task of human-related crimeclassification. In our proposed approach, the human body in video frames,represented as skeletal joints trajectories, is used as the main source ofexploration. First, we introduce the significance of extending the ground truthlabels for HR-Crime dataset and hence, propose a supervised and unsupervisedmethodology to generate trajectory-level ground truth labels. Next, given theavailability of the trajectory-level ground truth, we introduce atrajectory-based crime classification framework. Ablation studies are conductedwith various architectures and feature fusion strategies for the representationof the human trajectories. The conducted experiments demonstrate thefeasibility of the task and pave the path for further research in the field.

Crime scene classification from skeletal trajectory analysis in surveillance settings