Visual-based Vehicle Research Articles

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.

Vehicle Detection by Fusing Part Model Learning and Semantic Scene Information for Complex Urban Surveillance.

Visual-based vehicle detection has been studied extensively, however there are great challenges in certain settings. To solve this problem, this paper proposes a probabilistic framework combining a scene model with a pattern recognition method for vehicle detection by a stationary camera. A semisupervised viewpoint inference method is proposed in which five viewpoints are defined. For a specific monitoring scene, the vehicle motion pattern corresponding to road structures is obtained by using trajectory clustering through an offline procedure. Then, the possible vehicle location and the probability distribution around the viewpoint in a fixed location are calculated. For each viewpoint, the vehicle model described by a deformable part model (DPM) and a conditional random field (CRF) is learned. Scores of root and parts and their spatial configuration generated by the DPM are used to learn the CRF model. The occlusion states of vehicles are defined based on the visibility of their parts and considered as latent variables in the CRF. In the online procedure, the output of the CRF, which is considered as an adjusted vehicle detection result compared with the DPM, is combined with the probability of the apparent viewpoint in a location to give the final vehicle detection result. Quantitative experiments under a variety of traffic conditions have been contrasted to test our method. The experimental results illustrate that our method performs well and is able to deal with various vehicle viewpoints and shapes effectively. In particular, our approach performs well in complex traffic conditions with vehicle occlusion.