DetSPose: Detection of suspicious behavior by analyzing changes in people’s body posture

Abstract

This research work develops an automatic system for the detection of suspicious attitudes through computational analysis of body postures, using the MediaPipe library to extract and process the anatomical landmarks of the human skeleton. The study starts from the fundamental premise that certain postural patterns, movement sequences, and biomechanical configurations can reveal potentially hostile intentions before they materialize into explicit actions, all with the aim of complementing current surveillance systems. The proposed system is trained using the MediaPipe algorithm for obtaining human skeletal landmarks in both two- and threedimensional spaces, considering three experimental scenarios: the detection of a hidden hand behind the body, the detection of a hand hidden under clothing at the front of the torso, and the recognition of suspicious gaze patterns. The system is evaluated using accuracy, precision, recall, and F1 score metrics. The results demonstrate high performance in all metrics and scenarios using three-dimensional spaces (95.6%, 95.7% and 97.3% respectively for accuracy metric). It suggests that the proposed system offers significant potential for applications in real security environments.