TrackNetV4: Enhancing Fast Sports Object Tracking with Motion Attention Maps

Arjun Raj1,    Dr. Lei Wang1, 2,    Dr. Tom Gedon3
1Australian National University     2Data61/CSIRO     3Curtin University
Paper Code (coming soon) Dataset (coming soon)

Introduction

Abstract Section Image

We propose using learnable motion attention maps to enhance the tracking of high-speed, small objects in video frames. While demonstrated with TrackNetV2, our approach can be seamlessly integrated into any heatmap-based detection and tracking framework.
Additionally, our multi-ball tracking dataset encompasses singles and doubles matches, featuring challenges such as multiple courts in a single video, multiple balls in play, nighttime matches, and balls camouflaged or blending with the court's color. It also includes varying resolutions to enhance tracking robustness.

Abstract

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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BibTeX Citation

Here is the BibTeX entry for referencing our work:

@INPROCEEDINGS{tracknetv4,
  author={Raj, Arjun and Wang, Lei and Gedeon, Tom},
  booktitle={ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
  title={TrackNetV4: Enhancing Fast Sports Object Tracking with Motion Attention Maps}, 
  year={2025},
  volume={},
  number={},
  url={https://arxiv.org/abs/2409.14543}
}