Ball Tracking in Sports Analytics Using TrackNet V2 and V3

2. BALL TRACKING TECHNOLOGY OVERVIEW

2.1 Historical Perspective and Evolution

Ball tracking in sports analytics has done the most exemplary evolution over the years. This model was traditionally a manual approach of recording movement of balls as recorded in the match. Innovatively at the time, however, it did have definite limitations due to human perception and the susceptibilities caused due to fatigue, reaction times, and biases. Besides, manual tracking presented general thoughts, focusing usually at the top level of statistics rather than specific detailed data points.

With the progress in computer vision and AI, automated tracking became feasible, which surely outperformed precision and consistency. These days, deep learning models like Tracknet, besides giving a new benchmark for accuracy, make it possible for real-time tracking in competitive sport contexts.

2.2 Highly Relevant in Modern Sports Analytics

Modern sports are based on data-driven decision-making to enhance performance. Among the data centric approaches, ball tracking stands out, as it provides thorough and actionable insights into the dynamics of a game. Accurate capture of ball position, speed, trajectory, and interactions with the players forms a goldmine for coaches, analysts, players, and broadcasters. For coaches, ball-tracking data would form a basis for improving strategies and tactics.

Coaches can identify patterns during a play, such as the striking motion of the ball, passing accuracy, offensive and defensive formations, and shot selection, by analyzing the trajectory of the ball and its contact with the field. This allows them to personalize training sessions based on strengths and weaknesses for enhanced performance. For instance, in tennis, coaches can analyze a player's serve placement, spin, and speed to make just the right adjustments to the technique and strategy.

For example, ball-tracking data greatly benefits players. A sharper view of how players' bodies move and interact with the ball grants them the opportunity to make some adjustments pertaining to their training sessions based on data. It will mean that the player can develop better choices, quicker reaction times, and outcomes as compared to before playing the game. Also, ball-tracking data avails visualization in action, thereby creating awareness in areas of potential improvement, which raises one's motivation and also creates interest.

2.3 Challenges and Opportunities

In contrast, although ball tracking has enhanced significantly over the years, there are still various challenges. The range of challenges said to be inherent suggests that there is a complexity in tracking a dynamic sports environment moving object that is yet to be addressed.

Issues

1. Occlusions: One of the common problems usually encountered in ball tracking is occlusion. Occlusion here refers to the obstruction of the ball by a player, equipment, or other objects for a short period. Such obstructions would prevent the tracking system from efficiently detecting and tracking every movement the ball goes through. For instance, in a highly populated football game, the running ball might find itself hidden behind other players, hence likely producing tracking mistakes.
2. Rapid Motion: The ball moves very fast in most sports, and tracking it is very tricky. This causes a lot of motion blur effects, where the ball appears like a streak along the video frames, especially when moved at such high speeds. It captures and precisely predicts the position of a ball on real-time video with advanced processing capabilities and with predictive algorithms.
3. Changing Lighting Conditions: Sports activities are usually held under various illumination conditions: from full sun lighting to sports halls with low illumination. Illumination variation can affect visibilities of the ball at flight and impose difficulties in maintaining consistent precision of the tracking process.
4. Complex Interactions: The interactions of a ball with the players, the surface of play, or elements can be quite complex and difficult to predict. A ball may bounce in such a manner off a player or an object that no one foresees; therefore, tracking systems have to adapt rapidly to maintain accuracy.

Opportunities

Still, these challenges serve only to drive innovations in ball tracking technology. For example, the Tracknet V3 model is more sophisticated than its predecessors and represents an effort to address these challenges in an attempt to make the tracking technology better.

1. Improved Handling of Occlusions: In Tracknet V3, modern models employ more advanced algorithms to predict the location of the ball under temporary occlusion. Predictive modeling, Kalman filters, and even frame interpolation help to maintain smooth tracking when the ball is entirely occluded.
2. Processing speed and accuracy: Advances in deep learning architecture and optimization techniques have led to much faster and more accurate ball tracking. Moreover, the enhanced processing power of Tracknet V3 has allowed for the manipulation of high-speed motion and rapidly changing scenarios to ensure reliable real-time tracking in sports environments.
3. Adaptive Light Control: Advanced models adapt the method to changing lighting conditions using adaptive background subtraction and color normalization techniques. This would hence ensure consistent tracking regardless of the lighting conditions.
4. Real-Time Data Integration: This would then be integrated with other analytics, such as player movement data and game statistics, and give a better chance of understanding the game dynamics, hence offering a chance for more holistic insights through the combination of various data flows.

3. METHODOLOGY

3.1 Training the Model on a Custom-Built Dataset

The fundamental component in developing any AI-based track system is to generate a high-quality training dataset targeted toward the challenge at hand. In this work, the dataset for the ball-tracking TrackNet V2 and V3 models follows this structure:

3.1.1 Data Collection:

• The dataset is composed of extended footage from various games. Videos are included containing different sports, angles of the camera used, lighting conditions, resolutions, and game settings so that the model will run well across various scenarios.
• Specific scenarios, such as ball interactions near boundaries, fast-paced actions, or cases with player occlusion, are also attempted to try to increase robustness.

3.1.2 Data Labeling:

• Precise frames were carefully annotated by humans and labeled with the ball location, which was the equivalent of its real position. With this data on ground truth, the model learned how to predict accurate ball positions.
• Advanced labeling tools may be applied to speed up and also render uniformity; nevertheless, validation steps eliminate mistakes. The annotation data includes features of the ball such as coordinates, velocity vectors, and dimensions of the bounding box that would provide rich data points for training.

3.1.3 Training Process:

• The dataset is fed into the TrackNet V2 and V3 models, employing deep learning techniques to optimize the model’s weights for accurate ball detection and tracking.
• Data augmentation methods, such as random rotation, scaling, flipping, and motion blur are applied to further increase the variability of the dataset while enhancing model robustness in diverse real-world conditions.
• Fine tunes the model by using loss functions, optimizers for instance, Adam or stochastic gradient descent, and regularization techniques such as dropout and batch normalization.

3.2 Background Subtraction

Background subtraction is an important step to separate the ball from other static objects in the image sequence:

3.2.1 Objective:

This separative step separates moving objects, say the ball from a static background. This makes it possible for the model to devote its computing potential at interesting parts of the scene. Effective subtraction of the background reduces noise and minimizes the search space, thereby maximizing detection accuracy.

3.2.2 Techniques:

• Gaussian Mixture Models (GMM): The statistical method models each pixel as a mixture of Gaussian distributions to discern what can be foreground - moving objects-and what will be background. GMM adapts to slow changes in the background, such as gradual lighting changes.
• Adaptive Thresholding: Dynamically set thresholds help to differentiate between fore and background based on pixel intensity differences.
• Morphological Operations: Dilation and erosion techniques can be used to clean up the foreground mask by filling in holes in the objects detected and removing little noise.

3.2.3 Challenges and Solutions:

Complex background scenes, such as crowded and dynamically changing backgrounds will produce false positives. The background models are periodically updated, and color filters may be used in addition to improve the accuracy of separation.

3.3. Ball Candidate Filtering

Once moving objects are detected, filtering potential candidates ensures that only plausible ball positions are retained

3.3.1 Size and Shape Constraints:

• The size and shape attributes of the ball are predefined on sport-specific parameters. For example, the diameter and color of a tennis ball are significantly more different than those of a cricket ball.
• Detected objects are checked for these attributes. Objects not falling into the expected range get filtered out, thereby minimizing false positives.

3.3.2 Speed and Motion Constraints:

• The velocity and the trajectory of movement of the detected objects is estimated. The model uses past frames to estimate the expected path, and the corresponding expected speed of the ball. Candidates having significant discrepancies in these expected values are filtered out.
• A detected object moving at an abnormally low speed is noise and is filtered out. An object that maintains a constant trajectory with speed is retained.

3.3.3 Color and Texture Matching (Optional):

More complex filtering methods may proceed with color and texture matching between detected objects and predefined templates.

3.4 Frame-Based Movement Chaining

Movement chaining establishes connections between identified ball positions in subsequent frames to form a continuous trajectory:

3.4.1 Temporal Coherence:

The track follows the ball positions over successive frames by utilizing the temporal information. From the existing speed and direction, in combination with momentum, the model predicts where the ball will be at the next frame.

3.4.2 Tracking Algorithms:

• Kalman Filtering: The mathematical filter prediction of the next state of the moving object based on the observations of past ones that consider all noise and uncertainties in detection will give a smoothed trajectory.
• Particle Filters: In case the ball's motion is very complicated, particle filters could infer a probability distribution over possible positions of the ball across multiple paths, thus raising reliability in tracking.

3.4.3 Dealing With Sudden Movements

For most sports, direction change tends to be very jerky and quick because of player involvement. Using the past motion data as well as sharp changes in trajectory as cues, the model dynamically adjusts its prediction.

3.5 Frame-Based Image Interpolation

Image interpolation is crucial to ensure continuity in tracking the ball during moments of occlusion or when the ball goes out of the frame for a brief moment:

3.5.1 Objective

When the ball temporarily leaves the field of view due to the presence of a player or goes out of the frame, continuity of tracking needs to be maintained. The technique used in this sense is image interpolation, which gives the prediction of the location of the ball in the gaps based on surrounding frames.

3.5.2 Interpolation Techniques:

• Linear Interpolation:The simpler solution predicts the ball's position directly as a straight line between its last known and next detected positions. This method works pretty well for short occlusions.
• Spline-Based Interpolation:If there is a more complex trajectory, spline interpolation can produce smooth curves that can better describe the motion of the ball with consideration of acceleration, deceleration, and change in directions.

3.5.3 Error Correction:

Such positions are checked against subsequent detections to minimize errors. Should significant deviations be found, the interpolation process is adjusted or the prediction is discarded in favor of accuracy.

4. RESULTS AND EVOLUTION OF THE BALL TRACKING PROCESS

4.1 Ground truth annotation and comparison

We manually annotated the frames and compared them with the model's predictions to assess the performance of TrackNet V2 and V3.

• Manual Annotation:In manual annotation, frames are annotated to indicate the precise position of the ball in the ground plane and a bounding box is defined around each frame. This is the benchmark or "ground truth" data.
• Model Annotation: Model V2 and V3 produced the bounding box predictions of the ball location in each frame within TrackNet. These predictions were extracted and were compared against the ground truths using the measure IoU, which means Intersection over Union.

For each frame of the video, the IoU between the ground truth and model-predicted bounding box was obtained. The higher the IoU is close to 1, the higher is its overlap and the accuracy is better.

• The average IoU for all scenarios in TrackNet V2 is 0.82, while the most challenging situations are the rapid motion and occlusion.
• TrackNet V3 achieved much superior results, averaging 0.91 average IoU, showing significant improvements over the handling of occlusions and dense backgrounds in the images.

4.2 Handling Multiple Detects per Single Frames

In the evaluation process, there were some frames wherein the model detected several balls and needed postprocessing for enhanced outputs. The further steps that follow are:

• Size and Shape Filtering: This filter was already defined by the size and shape of the ball, hence most false positives have been eliminated because objects detected at this stage did not meet the physical attributes of a ball.
• Motion-Based Filter: We inspected the trajectory and speed of the detected object. Detections that had objects moving erratically or unrealistically were rejected. Example: Objects that move very far away from the predicted path of the ball were excluded.
• Confidence Scoring: The model gave every detection a confidence score. Only such detections greater than a predetermined threshold are kept, and hence, there is an element of dependability in the result.

4.3 Interpolation of Occluded Frames

Interpolation methods were developed to preserve the tracking even if the ball was occluded or had completely left the field of view.

• Linear Interpolation: For occlusions lasting from 1 to 3 frames, a straight-line prediction between the ball's last known and next detected positions was generated. This gave an easy though effective fix for short occlusions.
• Spline Interpolation: For occlusions between 4–7 frames, in which the ball's trajectory was changing due to acceleration or changes in direction, spline interpolation was used, and it thus made smoother predictions in more complex situations.
• Validation and Error Correction. The interpolated positions are consistent with subsequent detections. Frames exhibiting significant departures from the expected paths are recomputed or purged to prevent errors.

5. CONCLUSION

Ball tracking is turning out to be a groundbreaking technology in the sports analytics world, fundamentally changing from analysis and strategy to optimization of performance. TrackNet V2 and V3 are advanced deep learning models leading the way with unprecedented precision and efficacy in ball tracking. TrackNet V3 has tackled most of the major issues related to occlusions, fast motion, and varying lighting that have been taken as the decisive factors that were considered to prevent reliability in tracking even the most dynamic and complex sports environment.

Integrating ball tracking into sports analytics has made it possible to achieve insights that are far deeper than understanding the game dynamics, from player-ball interactions and team strategies to the opponents moves. The role of data has taken on a new dimension to be measurable, allowing for improvement in technique, training sessions, and general game performance.

Furthermore, ball tracking serves to enhance the experience for both fans and broadcasters, offering enriched visualizations and rendering complex game strategies more accessible. Its impact transcends the confines of the field, fostering increased engagement with sports and transforming the manner in which they are perceived and enjoyed.

6. What Can You Take Away from This White Paper?

• Game Changing Impact of Ball Tracking: Ball tracking technology has impacted the game in ways that relate fundamentally to how analytics about sports are thought of. It ensures plays of various sports are closely followed and accurately represented with actionable insights.
• Advanced Deep Learning Models: Advanced models such as TrackNet V2 and V3 represent the potential for artificial intelligence within the sports realm. The new models ensure real-time tracking under challenging conditions while improving accuracy and robustness.
• Improved Strategy and Performance: Accurate ball tracking provides coaches and players with an enhanced capability in terms of information gathered about the ball's flight, interaction, and flow, thereby allowing for more efficient training and strategic planning.
• Overcoming Challenges: To overcome common problems related to occlusions, rapid movement, and changes in illumination, new techniques involve advanced postprocessing, predictive modeling, and interpolation.
• Enriching Fan Engagement: That is how enriched visualizations like heatmaps, trajectories, and speed indicators may enrich fan engagement in games and eventually deepen their association with sports. Improvements into integrating with player tracking and team dynamics mean real-time data analytics. Such trajectories promise increasingly sophisticated insights and applications in ball tracking within near futures.
Ball tracking is not a technology by itself; it is one of those developments that form the basis of modern sports analytics. It offers windows into the intricate dynamics of the field, allowing teams, players, and fans with fact-driven perspectives to better understand, strategize, and enjoy the game.