Multi-Perspective Tracking for Intelligent Vehicle

The multi-camera array has drawn attention of researchers in recent years, and has been configured and deployed on intelligent vehicle to capture the panoramic views. Understanding surroundings is crucial for the ego-vehicle. This paper presents a Multi-perspective Tracking (MPT) framework for intelligent vehicle. An iterative search procedure is proposed to associate detections and tracklets in different perspectives. This procedure iteratively assigns determined states and estimates non-determined states for the detections and tracklets. An inherent determined and non-determined graph is utilized to reinforce this procedure. For more reliable associations between perspectives, a Siamese convolutional neural network is employed to learn feature representation. The supervised classification and verification signals are added to train the network. The features in different conventional stages are integrated together as the discriminative appearance model. The experiments are conducted on a MPT data set with five perspectives. The proposed framework is tested in each pair of adjacent perspectives for the ability to associate target objects between perspectives.