Real-time vehicle detection using parts at intersections

In this study, we propose a novel, lightweight approach to real-time detection of vehicles using parts at intersections. Intersections feature oncoming, preceding, and cross traffic, which presents challenges for vision-based vehicle detection. Ubiquitous partial occlusions further complicate the vehicle detection task, and occur when vehicles enter and leave the camera´s field of view. To confront these issues, we independently detect vehicle parts using strong classifiers trained with active learning. We match part responses using a learned matching classification. The learning process for part configurations leverages user input regarding full vehicle configurations. Part configurations are evaluated using Support Vector Machine classification. We present a comparison of detection results using geometric image features and appearance-based features. The full vehicle detection by parts has been evaluated on real-world data, runs in real time, and shows promise for future work in urban driver assistance.