Self-Supervised Interleaved Motion Representation Learning for Long-Range Sports Video Analytics

Abstract

Long-range sports video analytics presents unique challenges due to the need to capture fine-grained motion patterns over extended temporal horizons while maintaining computational efficiency and robustness to domain shifts. Traditional supervised approaches require extensive human annotation and often fail to generalize across different sports, camera setups, and environmental conditions. This paper proposes a self-supervised interleaved motion representation learning framework that leverages hierarchical multi-stream architectures to encode motion at multiple temporal scales without reliance on labeled data. The framework integrates contrastive and predictive self-supervised objectives within an interleaved encoder design, enabling the model to learn structured representations that disentangle short-term dynamics from long-term dependencies. System-level considerations including architectural trade-offs between model capacity and inference speed, the role of data augmentation and negative sampling strategies, and the implications for deployment on edge devices are examined. Furthermore, the paper addresses issues of fairness, such as demographic biases in broadcast sports data, and discusses governance frameworks for responsible deployment in automated coaching and officiating assistance. Experimental evaluations on benchmark sports video datasets demonstrate that the proposed approach achieves competitive performance on downstream tasks including action recognition, event localization, and player trajectory prediction. The work contributes a scalable, annotation-free paradigm for long-range video understanding and provides a critical analysis of the socio-technical infrastructure required for real-world adoption.