TrackNet: A Deep Learning Network for Tracking High-speed and Tiny Objects in Sports Applications (1907.03698v1)

Abstract: 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/.

• The paper presents a novel deep learning network that fuses CNN and FCN architectures to generate heatmaps for tracking high-speed, small objects.
• The methodology processes multiple consecutive frames to capture motion trajectories, overcoming challenges like blurriness and temporary invisibility.
• Results demonstrate high precision and recall in tennis and badminton, offering a cost-effective alternative for sports analytics.

TrackNet: A Deep Learning Network for Tracking High-speed and Tiny Objects in Sports Applications

The paper "TrackNet: A Deep Learning Network for Tracking High-speed and Tiny Objects in Sports Applications" introduces a novel framework designed to track fast-moving, small objects in sports using broadcast video data. Recognizing the challenge of tracking high-speed and minuscule objects like tennis or badminton balls, the authors propose a solution that capitalizes on deep learning techniques to address issues such as blurriness, afterimage tracks, and invisibility.

Summary and Methodology

TrackNet leverages a custom deep learning network that combines aspects of both Convolutional Neural Networks (CNN) and Fully Convolutional Networks (FCN). In particular, the network draws upon VGG-16 for feature extraction and incorporates deconvolutional layers to produce a heatmap for object tracking. This heatmap signals potential object locations across frames in video footage sourced from standard consumer-grade devices.

The model's architecture supports processing of multiple consecutive frames—an innovation enabling the network to discern and track movement patterns of objects based on their trajectories, even in cases where the object becomes briefly obscured or blurred. This feature significantly enhances SpotNet's utility in real-world, non-stationary video contexts, such as sports broadcasts, where the ball's speed and its consequent small appearance pose substantial challenges for conventional tracking techniques.

Results and Implications

TrackNet was evaluated using high-profile tennis matches like the men's singles final at the 2017 Summer Universiade. Impressively, the network achieved a precision of 99.7%, recall of 97.3%, and an F1-score of 98.5% during single-frame evaluations. When subjected to 10-fold cross-validation to counteract overfitting, TrackNet yielded 95.3% precision, 75.7% recall, and 84.3% F1-measure, underscoring the robust performance of the network.

For badminton, a sport characterized by even higher shuttlecock speeds than tennis, TrackNet still achieved commendable performance metrics of 85.0% precision and 68.7% F1-score. Notably, the network's capability to adapt to distinct sport-specific characteristics hints at a promising versatility for broader applications.

Practical and Theoretical Implications

Practically, this research contributes to making robust sports analysis more accessible and affordable, removing the dependency on expensive, high-specification cameras currently used in professional settings such as Hawk-Eye systems. The broader accessibility of TrackNet supports extensive sports tactical analysis and player performance evaluation without significant financial outlays.

Theoretically, this work expands the potential for deep learning applications in dynamic object recognition and tracking. TrackNet's integration of trajectory learning through consecutive frames represents a compelling advancement in harnessing temporal patterns within video data—a principle that could be applied to other domains requiring real-time, rapid object recognition in similarly challenging environments.

Future Developments in AI

Moving forward, the methodology underpinning TrackNet could inspire enhancements in tracking technology across diverse fields beyond sports, such as autonomous vehicles or surveillance systems, where rapid and accurate object detection is critical. Further research could explore optimizing the network for different environmental conditions or expanding its use to other sporting or high-speed events.

In summary, TrackNet stands as a significant technological contribution to the tracking of high-speed objects, demonstrating both practical implications for sports data analytics and theoretical opportunities for advancing object detection in broader AI applications.