Accurate Vehicle Detection Using Multi-camera Data Fusion and Machine Learning

Computer-vision methods have been extensively used in intelligent transportation systems for vehicle detection. However, the detection of severely occluded or partially observed vehicles due to the limited camera fields of view remains a challenge. This paper presents a multi-camera vehicle detection system that significantly improves the detection performance under occlusion conditions. The key elements of the proposed method include a novel multi-view region proposal network that localizes the candidate vehicles on the ground plane. We also infer the vehicle position on the ground plane by leveraging multi-view cross-camera context. Experiments are conducted on dataset captured from a roadway in Richardson, TX, USA, and the system attains 0.7849 Average Precision and 0.7089 Multi Object Detection Precision. The proposed system results in an approximately 31.2% increase in AP and 8.6% in MODP than the single-camera methods.