Part-Aware Region Proposal for Vehicle Detection in High Occlusion Environment

Visual-based vehicle detection has been extensively applied for autonomous driving systems and advanced driving assistant systems, however, it faces great challenges as a partial observation regularly happens owing to occlusion from infrastructure or dynamic objects or a limited vision field. This paper presents a two-stage detector based on Faster R-CNN for high occluded vehicle detection, in which we integrate a part-aware region proposal network to sense global and local visual knowledge among different vehicle attributes. That entails the model simultaneously generating partial-level proposals and instance-level proposals at the first stage. Then, different parts belong to the same vehicle are encoded and reconfigured into a compositional entire proposal through a part affinity fields, allowing the model to generate integral candidates and mitigate the impact of occlusion challenge to the utmost extent. Extensive experiments conducted on KITTI benchmark exhibit that our method outperforms most machine-learning-based vehicle detection methods and achieves high recall in the severely occluded application scenario.