A Person Re-identification Approach Focusing on the Occlusion Problem and Ranking Optimization

Person re-identification (Re-ID) aims to re-identify people across multiple video frames captured at various time instants. With the advancement of deep neural networks and increasing demand of intelligent video surveillance, recent works have proposed many deep learning based approaches to address the task. However, many state-of-the-arts Re-ID methods are not robust enough and do not perform well on the Synergy Re-Identification dataset provided by the DeepSportRadar Player Re-Identification Challenge due to the severe occlusion problem. To better re-identify basketball players, we propose a person Re-ID approach that focuses on the occlusion problem and ranking optimization. Specifically, our proposed approach consists of two stages. In the first stage we extract the global features and local features of the input image by two branches respectively. In the second stage, we propose a ranking optimization method that consists of three steps: k-reciprocal re-ranking, metric fusion, and distance mapping. We conduct extensive experiments to show that our proposed approach can achieve superior performance compared with the state-of-the-art methods and we also experimentally evaluate the training tricks and ranking optimization methods of existing Re-ID methods. Our proposed method achieves $98.38%$ mAP and $99.57%$ rank-1 on the challenge set of the Synergy Re-Identification dataset, and with this method our team, Fiery Tyrannosaurus Warrior, won the second place in the DeepSportRadar Player Re-Identification Challenge. Furthermore, we provide some ideas for potentially achieving better performance on the Synergy Re-Identification dataset.