Go with the Flow: Improving Multi-view Vehicle Detection with Motion Cues

As vehicles travel through a scene, changes in aspect ratio and appearance as observed from a camera (or an array of cameras) make vehicle detection a difficult computer vision problem. Rather than relying solely on appearance cues, we propose a framework for detecting vehicles and eliminating false positives by utilizing the motion cues in the scene in addition to the appearance cues. As a case study, we focus on overtaking vehicle detection in a freeway setting from forward and rear views of the ego-vehicle. The proposed integration occurs in two steps. First, motion-based vehicle detection is performed using optical flow. Taking advantage of epipolar constraints, salient motion vectors are extracted and clustered using spectral clustering to form bounding boxes of vehicle candidates. Post-processing and outlier removal further refine the detections. Second, the motion-based detections are then combined with the output of an appearance-based vehicle detector to reduce false positives and produce the final vehicle detections.