Temporal Deep Learning Anomaly Detection for Pedestrian Accident Detection in Video Surveillance

Pedestrian-vehicle collisions are a critical subset of anomaly detection in overhead traffic surveillance, where automated identification aids timely safety interventions. An object detector locates vehicles and pedestrians in each frame. Temporal tracking algorithms then refine trajectories to identify collision candidates. A convolutional neural network subsequently verifies collision events. Evaluation on the WTS dataset demonstrates high precision in collision detection, highlighting potential for automated pedestrian safety monitoring.