TrackNet: A Deep Learning Network for Tracking High-speed and Tiny Objects in Sports Applications
This addresses the challenge of accurate ball tracking for sports analytics, providing a solution for performance evaluation and strategy analysis, though it is incremental as it builds on existing deep learning methods for object tracking.
The paper tackles the problem of tracking high-speed, tiny tennis balls in broadcast videos, where balls are often blurry or invisible, by developing TrackNet, a deep learning network that achieves precision, recall, and F1-measure up to 99.7%, 97.3%, and 98.5% respectively on a specific dataset.
Ball trajectory data are one of the most fundamental and useful information in the evaluation of players' performance and analysis of game strategies. Although vision-based object tracking techniques have been developed to analyze sport competition videos, it is still challenging to recognize and position a high-speed and tiny ball accurately. In this paper, we develop a deep learning network, called TrackNet, to track the tennis ball from broadcast videos in which the ball images are small, blurry, and sometimes with afterimage tracks or even invisible. The proposed heatmap-based deep learning network is trained to not only recognize the ball image from a single frame but also learn flying patterns from consecutive frames. TrackNet takes images with a size of $640\times360$ to generate a detection heatmap from either a single frame or several consecutive frames to position the ball and can achieve high precision even on public domain videos. The network is evaluated on the video of the men's singles final at the 2017 Summer Universiade, which is available on YouTube. The precision, recall, and F1-measure of TrackNet reach $99.7\%$, $97.3\%$, and $98.5\%$, respectively. To prevent overfitting, 9 additional videos are partially labeled together with a subset from the previous dataset to implement 10-fold cross-validation, and the precision, recall, and F1-measure are $95.3\%$, $75.7\%$, and $84.3\%$, respectively. A conventional image processing algorithm is also implemented to compare with TrackNet. Our experiments indicate that TrackNet outperforms conventional method by a big margin and achieves exceptional ball tracking performance. The dataset and demo video are available at https://nol.cs.nctu.edu.tw/ndo3je6av9/.