超越单一裁剪：用于球员重识别的上下文嵌入
Beyond a Single Crop: Contextualized Embeddings for Player Re-Identification
AI总结：
针对广播体育视频球员重识别的挑战，提出ContextReID框架，结合空间与时间上下文学习，在三个多目标跟踪基准上提升了球员检索的Rank-1和mAP指标。
摘要：
行人重识别通常通过从单个行人裁剪区域学习身份判别性嵌入来解决，但广播体育视频中的球员重识别面临独特挑战：同一球队的球员常具有相似外观，而视频序列提供了同一球员在不同视角和周围场景下的重复观测。现有的基于裁剪的方法大多忽略了这种对区分视觉相似身份至关重要的上下文信息。本文提出ContextReID，一种上下文嵌入框架，通过联合考虑同一帧中的其他球员以及广播序列不同帧的观测来表示球员。通过结合共现球员和跨帧观测，该模型学习相对于周围视觉上下文的身份特征，而非仅依赖孤立的外观线索。我们用视觉语言模型实现该框架，该模型以一组球员裁剪区域为输入，为单个球员生成上下文感知嵌入。结合上下文学习和检索的目标函数，鼓励模型捕捉跨帧身份关系，同时保持用于球员匹配的判别性嵌入。在三个多目标跟踪基准上的实验表明，ContextReID在Rank-1和mAP指标上均优于传统的单一裁剪嵌入方法，这些结果凸显了空间和时间上下文在广播体育视频中可靠球员识别的实用性。
英文摘要：
Person re-identification is typically addressed by learning identity-discriminative embeddings from individual person crops. However, player re-identification in broadcast sports videos presents a unique challenge: players from the same team often share similar appearances, while video sequences provide repeated observations of the same players under different viewpoints and surrounding contexts. Existing crop-based approaches largely ignore this contextual information, which can be critical for resolving visually similar identities. Here, we propose ContextReID, a contextualized embedding framework that represents a player by jointly considering other players in the same frame and observations from different frames of the broadcast sequence. By incorporating both co-occurring players and cross-frame observations, the model learns identity features relative to the surrounding visual context rather than relying only on isolated appearance cues. We implement this framework with a vision-language model that takes sets of player crops as input and generates context-aware embeddings for individual players. A combined contextual learning and retrieval objective encourages the model to capture cross-frame identity relationships while maintaining discriminative embeddings for player matching. Experiments across three multi-object tracking benchmarks show that ContextReID improves player retrieval over conventional single-crop embedding approaches in both Rank-1 and mAP. These results highlight the utility of spatial and temporal context for reliable player identification in broadcast sports videos.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
