LAUSR

Pedestrian safety is a major challenge in geographies where the traffic is not well managed, such as developing nations. Pedestrians often lose their lives when moving vehicles or buses hit them, for example, nearly half the road mishaps in Delhi involve such scenarios. We envision that video analytics on dashboard cameras can solve this problem if the pedestrians could be detected and tracked within permissible limits to alert the vehicle drivers. In this paper, we propose a novel end-to-end pipeline for multi-pedestrian detection and tracking their trajectories by leveraging multi-scale aggregate channel features in a tracking-by-detection paradigm and temporal trajectory analysis for pedestrian trajectory characterization to identify if the pedestrian is on a potential collision course with the vehicle. We evaluate this framework on a novel dataset collected from dashboard cameras with frontal view of Delhi roads with unlaned and chaotic traffic conditions. The problem of multi-pedestrian detection, tracking and characterizing pedestrian behavior on the road becomes challenging because of the motion of camera mounted on-board the vehicle and different traffic scenarios on Delhi roads. Extensive experiments demonstrate that the proposed method achieves promising results over several pedestrian detection baselines on the collected dataset with an average precision of 94.71% in detection and an accuracy of 86.48% in pedestrian behavior classification.