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<p>Real-time multi-object tracking (MOT) in sports videos faces persistent challenges arising from dense player occlusion, rapid nonlinear motion, and the stringent computational constraints of edge devices. This paper presents an integrated framework that co-designs a lightweight detector, an adaptive tracking strategy, and an edge deployment pipeline to address these challenges jointly. We propose Sports-LiteDet, a compact detection network featuring a coordinate-attention-enhanced backbone and a bidirectional multi-scale feature fusion neck, which achieves 71.8% mAP@0.5 on sports test data at only 3.2 GFLOPs. For tracking, we introduce a motion-consistency association module coupled with adaptive keyframe scheduling and an asynchronous detection–tracking pipeline, attaining 68.3% MOTA and 71.6% IDF1 while nearly doubling throughput over synchronous baselines. A sequential compression strategy combining structured pruning and INT8 quantization reduces the model to 5.8&#xa0;MB, enabling 68.0 FPS inference on NVIDIA Jetson Orin Nano at under 10&#xa0;W power consumption. Extensive experiments on public sports benchmarks and self-collected sequences demonstrate that the proposed framework achieves competitive tracking accuracy with substantially lower latency compared to existing methods, validating its viability for field-deployable intelligent sports analytics.</p>

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Real-time multi-object tracking for sports scenarios: a lightweight detection and edge deployment co-design framework

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月17日

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  • JiangHao Jing

摘要

Real-time multi-object tracking (MOT) in sports videos faces persistent challenges arising from dense player occlusion, rapid nonlinear motion, and the stringent computational constraints of edge devices. This paper presents an integrated framework that co-designs a lightweight detector, an adaptive tracking strategy, and an edge deployment pipeline to address these challenges jointly. We propose Sports-LiteDet, a compact detection network featuring a coordinate-attention-enhanced backbone and a bidirectional multi-scale feature fusion neck, which achieves 71.8% mAP@0.5 on sports test data at only 3.2 GFLOPs. For tracking, we introduce a motion-consistency association module coupled with adaptive keyframe scheduling and an asynchronous detection–tracking pipeline, attaining 68.3% MOTA and 71.6% IDF1 while nearly doubling throughput over synchronous baselines. A sequential compression strategy combining structured pruning and INT8 quantization reduces the model to 5.8 MB, enabling 68.0 FPS inference on NVIDIA Jetson Orin Nano at under 10 W power consumption. Extensive experiments on public sports benchmarks and self-collected sequences demonstrate that the proposed framework achieves competitive tracking accuracy with substantially lower latency compared to existing methods, validating its viability for field-deployable intelligent sports analytics.