Exploring Loss Function and Rank Fusion for Enhanced Person Re-identification

Jun Yu, Renda Li, Renjie Lu, Leilei Wang, Shuoping Yang, Lei Wang, Minchuan Chen, Qingying Zhu, Shaojun Wang, Jing Xiao

Abstract: Person Re-Identification (Re-ID) emerges as a important technique in sports analytics, enabling the accurate matching and recognition of players throughout a game. The fundamental objective of the Person Re-ID task is to identify the same player across diverse camera views, thus establishing their identity association over time. Generally speaking, the difficulty of the person Re-Identification (Re-ID) task lies in the perspective changes, occlusion phenomena, and posture changes caused by different camera placements and angles. In particular, for the Synergy re-identification dataset, the overlapping occlusion phenomenon between players and the low resolution and motion blur of the image make the Re-ID task challenging. In this paper, we analyze the impact of different data augmentations on this dataset and find effective augmentation methods. Meanwhile, we adopt a contrastive image-to-image training method and achieve higher results with the class-independent InfoNCE loss. We also quantitatively compare it with class-related ID loss. Finally, we employ the k-reciprocal re-ranking method to reorganize and optimize the distance matrix output by the model. The above process enables a single model to have good retrieval performance on the Synergy re-identification dataset. Then, we summarize the key factors affecting the retrieval performance on this dataset. To further improve the retrieval performance, we propose an efficient model/rank fusion method to fuse the retrieval results of different models from two perspectives of similarity and dissimilarity. Our proposed method achieves 98.81% mAP on the challenge set of the Synergy re-identification dataset, with which our team achieved 1st place in the DeepSportRadar player re-identification challenge 2023.