Vehicle Detection by Independent Parts for Urban Driver Assistance

In this paper, we introduce vehicle detection by independent parts (VDIP) for urban driver assistance. In urban environments, vehicles appear in a variety of orientations, i.e., oncoming, preceding, and sideview. Additionally, partial vehicle occlusions are common at intersections, during entry and exit from the camera's field of view, or due to scene clutter. VDIP provides a lightweight robust framework for detecting oncoming, preceding, sideview, and partially occluded vehicles in urban driving. In this paper, we use active learning to train independent-part detectors. A semisupervised approach is used for training part-matching classification, which forms sideview vehicles from independently detected parts. The hierarchical learning process yields VDIP, featuring efficient evaluation and robust performance. Parts and vehicles are tracked using Kalman filtering. The fully implemented system is lightweight and runs in real time. Extensive quantitative analysis on real-world on-road data sets is provided.