On-road vehicle detection through part model learning and probabilistic inference

Visual based approach has been studied extensively for on-road vehicle detection, while it faces great challenges, as visual appearance of a vehicle may change greatly across different viewpoints, and partial observation happens sometime due to occlusions from infrastructure or scene dynamics, and/or limited camera vision field. Inspired by the works on part-based detection, this research proposes a probabilistic framework for on-road vehicle detection, where focus is cast on vehicle pose inference on the set of part instances by addressing the issues of partial observation and varying viewpoints. To this end, geometric models describing the configuration of vehicle parts as well as their spatial relations in probabilistic representations are learned for each dominant viewpoint, and viewpoint maps are generated on each typical road structure, which provide probabilistic prediction to the viewpoints of a vehicle at each location at ego frame. Experiments have been conducted using a data set that was developed in the authors´ previous work on the ring roads in Beijing. Viewpoint-discriminative part appearance models (VDPAM) and viewpoint-discriminative part-based geometric models (VDPGM) are learned on the image samples of the data set, and the road structure-based probabilistic viewpoint maps (RSPVM) are generated by taking the statistics of the Lidar-based vehicle detection results. On-road vehicle detection is examined using an on-road video stream that has been labelled with ground truth. Experimental results are presented and efficiency on detecting the partially observed vehicles on varying viewpoints is demonstrated.