On-Road Pedestrian Tracking Across Multiple Driving Recorders

In this paper, we propose a new framework to track on-road pedestrians across multiple driving recorders. The framework is built upon the results of tracking under a single driving recorder. More specifically, we treat the problem as a multi-label classification task and determine whether a specific pedestrian belongs to one or several cameras' field of views by considering association likelihood of the tracked pedestrians . The likelihood is calculated based on the pedestrians' motion cues and appearance features, which are necessarily transformed via brightness transfer functions obtained by some available spatially overlapping views for compensating diversity of the cameras. When a pedestrian is leaving a camera's field of view, the proposed framework predicts and interpolates its possible moving trajectories, facilitated by open map service which can provide routing information. Experimental results show the robustness and effectiveness of the proposed framework in tracking pedestrians across several recorded driving videos. Moreover, based on the GPS locations, we can also reconstruct a 3-D visualization on a 3-D virtual real-world environment, so as to show the dynamic scenes of the recorded videos.