Partially occluded vehicle recognition and tracking in 3D

Vehicle detection is a key problem in computer vision, with applications in driver assistance and active safety. A challenging aspect of the problem is the common occlusion of vehicles in the scene. In this paper, we present a vision-based system for vehicle localization and tracking for detecting partially visible vehicles. Consequently, vehicles are localized more reliably and tracked for longer periods of time. The proposed system detects vehicles using an active-learning based monocular vision approach and motion (optical flow) cues. A calibrated stereo rig is utilized to acquire a depth map, and consequently the real-world coordinates of each detected vehicle. Tracking is performed using a Kalman filter. The tracking is formulated to integrate stereo-monocular information. We demonstrate the effectiveness of the proposed system on a multilane highway dataset containing instances of vehicles with relative motion to the ego-vehicle.