Integrating motion and appearance for overtaking vehicle detection

The dynamic appearance of vehicles as they enter and exit a scene makes vehicle detection a difficult and complicated problem. Appearance based detectors generally provide good results when vehicles are in clear view, but have trouble in the scenes edges due to changes in the vehicles aspect ratio and partial occlusions. To compensate for some of these deficiencies, we propose incorporating motion cues from the scene. In this work, we focus on a overtaking vehicle detection in a freeway setting with front and rear facing monocular cameras. Motion cues are extracted from the scene, and leveraging the epipolar geometry of the monocular setup, motion compensation is performed. Spectral clustering is used to group similar motion vectors together, and after post-processing, vehicle detections candidates are produced. Finally, these candidates are combined with an appearance detector to remove any false positives, outputting the detections as a vehicle travels through the scene.