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TrackNetV4: Enhancing Fast Sports Object Tracking with Motion Attention Maps

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arxiv 2409.14543 v1 pith:VMRWHXRS submitted 2024-09-22 cs.CV cs.AIcs.LG

Arjun Raj , Lei Wang , Tom Gedeon This is my paper · ORCID
classification cs.CVcs.AIcs.LG
keywords motiontrackingballmapsattentionfeatureslikeocclusion
verification ladder T0 review T1 audit T2 compute T3 formal
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Accurately detecting and tracking high-speed, small objects, such as balls in sports videos, is challenging due to factors like motion blur and occlusion. Although recent deep learning frameworks like TrackNetV1, V2, and V3 have advanced tennis ball and shuttlecock tracking, they often struggle in scenarios with partial occlusion or low visibility. This is primarily because these models rely heavily on visual features without explicitly incorporating motion information, which is crucial for precise tracking and trajectory prediction. In this paper, we introduce an enhancement to the TrackNet family by fusing high-level visual features with learnable motion attention maps through a motion-aware fusion mechanism, effectively emphasizing the moving ball's location and improving tracking performance. Our approach leverages frame differencing maps, modulated by a motion prompt layer, to highlight key motion regions over time. Experimental results on the tennis ball and shuttlecock datasets show that our method enhances the tracking performance of both TrackNetV2 and V3. We refer to our lightweight, plug-and-play solution, built on top of the existing TrackNet, as TrackNetV4.

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Cited by 3 Pith papers

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  1. Evolving Skeletons: Motion Dynamics in Action Recognition

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    Taylor-transformed skeletons improve ST-GCN accuracy but reduce Hyperformer accuracy on NTU-60/120, indicating that motion-injected inputs do not universally benefit skeleton-based action recognition models.

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    A survey arguing that video-LLMs rely on pretrained encoders and short-biased datasets, leaving them weak at long-term temporal reasoning such as causality and event progression.

  3. Quo Vadis, Anomaly Detection? LLMs and VLMs in the Spotlight

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    A survey of 13 recent LLM/VLM-based video anomaly detection methods, organized by interpretability, temporal modeling, few-shot learning, and open-world detection.

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