Summary of Tracknetv4: Enhancing Fast Sports Object Tracking with Motion Attention Maps, by Arjun Raj et al.
TrackNetV4: Enhancing Fast Sports Object Tracking with Motion Attention Maps
by Arjun Raj, Lei Wang, Tom Gedeon
First submitted to arxiv on: 22 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes an enhancement to the TrackNet framework for detecting and tracking high-speed objects like tennis balls and shuttlecocks in sports videos. The authors identify limitations in current deep learning models that rely heavily on visual features without incorporating motion information, which is crucial for precise tracking and trajectory prediction. To address this, they introduce a motion-aware fusion mechanism that fuses high-level visual features with learnable motion attention maps to emphasize the moving object’s location and improve tracking performance. The method leverages frame differencing maps modulated by a motion prompt layer to highlight key motion regions over time. Experimental results on tennis ball and shuttlecock datasets show that the enhanced TrackNetV4 outperforms existing TrackNet versions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us track moving objects like balls in sports videos better. Right now, current computer models are not good at this because they only focus on what things look like, without considering how they move. The authors of this paper want to fix this by combining information about what the object looks like with information about its motion. They created a new way to do this that works well for tracking tennis balls and shuttlecocks. |
Keywords
» Artificial intelligence » Attention » Deep learning » Prompt » Tracking
