Detecting partially occluded vehicles with geometric and likelihood reasoning

Abstract

In real-world scenes, vehicles are frequently overlapped by other objects and various backgrounds. In this study, an effective method to detect such vehicles, especially those partially occluded by nearby vehicles or other objects is presented. The authors have developed a statistical approach to generate occlusion hypothesis and a new hypothesis verification method. To verify occlusion hypothesis, the verification method utilises geometric and likelihood information. In this way, both vehicle-background and vehicle-vehicle occlusions can be detected. No additional occlusion-specific training is required. In addition, a median filter is applied to eliminate the noise in the patch scoring, and a union-find algorithm is used to find the connected positive region in the binary map. A synthesised occlusion dataset is created to test the performance, and the experimental results on popular benchmarks indicate that the proposed method is effective and robust in recognising partially occluded vehicles.