Beyond a Single Crop: Contextualized Embeddings for Player Re-Identification
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.
ProofPaper ↗
Key points
- 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.
- A combined contextual learning and retrieval objective encourages the model to capture cross-frame identity relationships while maintaining discriminative embeddings for player matching.
- These results highlight the utility of spatial and temporal context for reliable player identification in broadcast sports videos.
Sources (1)
- [1]Beyond a Single Crop: Contextualized Embeddings for Player Re-IdentificationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 09:07 AM
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.
Person re-identification is typically addressed by learning identity-discriminative embeddings from individual person crops.
Extractive summary: sentences quoted from the sources.