Computer Vision Services for Sport
Sports Tracking and Events Data
We implement a workflow for collecting, managing, and preparing sports videos for processing by automatic tracking models. This process ensures the efficiency and scalability of the treatments, as well as compliance with the required legal and technical standards.
VIDEO DOWNLOAD (broadcast TV or tactical plan)
> Selection of sources and automation of downloads:
Defining Needs: Identify the types of sports, leagues, and competition levels relevant for analysis needs.
Platform Choices: Select broadcast platforms or online services that offer access to high-definition match videos, such as sports television channels or sports streaming services.
Download Tools: Use of automated video downloading tools.
Planning: Set up automated tasks using, for example, cron jobs to execute these downloads at regular intervals, especially right after matches to capture replays.
> Respect for Copyright:
Licenses and Permissions: Check the necessary rights to download and use the videos for analyses, especially if the results are to be shared publicly or used commercially.
MANAGEMENT AND PREPARATION OF VIDEO DATASETS
> File Organization:
File System: Create a consistent folder structure by date, type of sport, and event.
Metadata: Store relevant information such as match dates, teams, or the final score in a metadata file associated with each video.
> Video Preprocessing:
Format Conversion: Convert videos to a uniform format compatible with tracking tools.
Resolution and Frame Rate: Convert videos to meet specific resolution and frame rate requirements of tracking models.
Data Cleaning: Use video editing tools to cut unnecessary segments, remove advertisements, and prepare clips focused solely on the game.
> Indexing and Access:
Database: Integrate video files and their associated metadata into a database to allow for quick searches and efficient access.
Backup and Security: Implement regular backup systems to protect your videos and metadata against data loss.
> Preparation for Tracking:
Preliminary Annotations: For certain tracking models, manually annotate portions of videos to train the initial models in supervised learning.
Validation Tests: Conduct spot checks to validate the preprocessed videos in the tracking systems.
We maximize the efficiency of automatic tracking models by ensuring that all necessary data are exhaustively collected and that the model is precisely configured for the specific context of the match. This ensures the accuracy of movement tracking.
COLLECTING SPECIFIC MATCH DATA
> Identification of Needed Information:
Basic Elements: Date, time, location, competition (e.g., league, tournament).
Team Details: Team names, team compositions, formations, player changes.
Match Conditions: Weather conditions, field conditions, lighting.
Match Events: Key moments such as goals, fouls, cards, substitutions.
> Means of Collection:
Official Data Sources: Use websites of leagues, clubs, or sports organizations to obtain official information.
Broadcasts and Commentary: Listen to match commentaries to capture unwritten details available in official statistics.
Live Data Collection Tools: Use apps for real-time event scoring.
> Information Storage:
Database: Organize and store information in a structured database to facilitate future analysis and quick access.
Association with Videos: Link data sets to the corresponding video file to simplify the tracking and analysis process.
TRACKING MODEL SETUP
> Camera Calibration and Field Dimensioning:
Type of Camera Identification: Fixed camera, pan-tilt-zoom (PTZ), or drone to adjust tracking settings.
Camera Parameters: Collect technical specifications such as view angle, resolution, and zoom metadata to calibrate the model.
Boundary Recognition: Use shape detection algorithms or manually annotate field boundaries in the initial images to define the play area.
Field Size: Identify the actual dimensions of the field to accurately estimate distances and positions.
> Video Pre-processing and Tracking Setup:
Normalization: Manage the brightness, contrast, and other visual aspects of videos so that the images are uniform for deep learning-based models.
Masking: Apply masks to exclude out-of-play areas and reduce distractions for the tracking model.
Parameter Initialization: Adjust tracking settings such as the size of objects to track (players, ball), detector sensitivity, and tracking algorithms (such as Kalman Filter, Hungarian Algorithm, etc.).
Preliminary Training: For supervised learning models, train the model on an annotated dataset to improve its accuracy.
Trial on Test Clips: Before deploying the model in real situations, test it on video clips from the same sport to verify tracking accuracy.
Adjustments and Optimization: Make necessary adjustments based on the test results to optimize the model's accuracy.
These steps implement a system for tracking and analyzing sports performance that not only captures and follows player movements with precision but also provides in-depth tactical and strategic analyses.
OBJECT DETECTION (players, ball, referees, etc.)
> Selection of Detection Model:
Choice of Algorithm: Use robust object detection models such as YOLO (You Only Look Once), SSD (Single Shot Multibox Detector), or Faster R-CNN, depending on the needs for accuracy and speed.
Customization: Adapt the selected model to specifically recognize players, the ball, and other relevant elements (such as referees) according to the specifics of the sport.
> Model Training:
Data Preparation: Collect and annotate training datasets that include match images clearly showing the objects to be detected.
Training Process: Use a Machine Learning platform like TensorFlow or PyTorch to train the model on the annotated data. Adjust the hyperparameters to optimize model performance.
> Validation and Optimization:
Performance Tests: Evaluate the model on a test dataset to verify its accuracy and detection capabilities.
Adjustments: Optimize the model by adjusting the parameters or re-training with additional data to improve detection rates and reduce false positives.
OBJECT TRACKING
> Initialization and Execution of Tracking:
Detector Association: Integrate the outputs of the detection model as a starting point for tracking.
Choice of Tracking Technique: Select a suitable tracking method, such as tracking based on the Kalman Filter, correlation tracking, or SORT (Simple Online and Realtime Tracking).
Trajectory Continuity: Use the chosen algorithms to maintain the identity of objects from one frame to the next, even in cases of temporary occlusions or rapid movements.
Occlusion Management: Implement mechanisms to handle occlusions, where a player might be temporarily hidden behind another object.
> Trajectory Optimization:
Error Correction: Review the generated trajectories to correct potential errors due to incorrect identifications or tracking losses.
Data Fusion: Combine multiple data sources or camera angles, if available, for a more robust and accurate trajectory.
We systematically collect match events to ensure the accuracy and reliability of information for post-match analysis and future tactical decisions. This involves a combination of manual and automated methods, utilizing both human skills and AI technology to capture and analyze sports data.
COLLECTION OF PASS TYPES AND GAME ACTIONS
> Definition of Pass and Action Categories:
Types of Passes: Assist, cross, or other types of passes (short, long, through, etc.).
Types of Actions: Decisive actions, actions within the penalty area...
> Annotation and Labeling Methods:
Manual Video Annotation: Analysis of match videos by expert annotators using specific tracking applications that identify and classify types of passes and game actions.
Specific Software: Tools like Hudl or Wyscout are used to segment and categorize game actions.
Expert Manual Review: Annotated videos are checked by experts or analysts to ensure their accuracy.
Automated AI Checks: Automated control procedures for passes and game actions can be implemented.
COLLECTION OF SCORE
> Real-Time or Delayed Tracking:
Automated Scoring: Use of applications to update scores in real time based on official data streams or manual entries during the match.
Manual Annotation: Manual scoring annotations on video.
Verification by Multiple Sources: Comparison of scores from different sources (e.g., league data, broadcast TV) to ensure accuracy.
COLLECTION OF FOULS, CARDS, AND REFEREE INTERVENTIONS
> Identification of Fouls and Rule Interpretations:
Foul Catalog: Definition of different types of fouls according to the rules of the game concerned and training of analysts.
Rule Interpretation: Specific annotations of certain game rules as needed for statistical analysis.
> Tracking Technologies:
Manual Video Annotation: Use of video annotation systems to mark each foul, yellow card, red card, and referee intervention.
Integrated Systems: Some models can automatically detect these incidents in synchronization with the video.
Centralized Database: Integration of information into a database to facilitate analysis and reporting.
COLLECTION OF TACTICAL CHOICES AND PLAYER SUBSTITUTION
> Tactical Analysis and Substitution Tracking:
Direct Observations: Recording of initial formations and tactical changes observed during the match, often identified by changes in player positions or styles of play.
Interviews with Coaches: Annotation of game stoppages, interviews with the coach, and subsequent tactical choices.
Temporal Marking: Temporal annotation of tactical events and substitutions.
Manual review of frames after automatic tracking is a critical step to ensure the accuracy and reliability of sports video tracking data. We correct errors and fill gaps left by automated systems, especially in complex situations such as occlusions or fine details that are difficult to capture automatically.
PREPARATION FOR MANUAL REVIEW
> Selection of Sequences to Review:
Automatic Analysis: Identify segments where the tracking model has signaled high uncertainties, track losses, or anomalies in the data such as abrupt and implausible changes in player movements.
Analyst Feedback: Incorporate feedback from analysts who use the data, often able to spot errors that algorithms do not detect.
Video Application: Use specific video annotation tools such as Anvil or ELAN, which allow for precise manipulation and annotation of videos frame by frame.
MANUAL FRAME REVIEW
> Jersey Numbers Correction:
Visual Identification: Ensure that all jersey numbers are correctly identified and linked to the names of the players.
Consistent Annotations: Manually mark the number on frames where automatic tracking has failed or generated errors using distinct colors or markers.
> Occlusions Management:
Occlusion Detection: Identify frames where a player is obscured by another player, an element of the field, or temporarily moves out of camera view.
Trajectory Reconstruction: Use logical inferences based on the player's previous and subsequent movements to reconstruct probable positions during the occlusion.
VALIDATION AND REPORTING OF CORRECTIONS
> Modifications Review and Integration:
Double Verification: Have the corrections reviewed by a second analyst to avoid errors of subjectivity or overcorrection.
Temporal Consistency: Ensure that corrected annotations are consistent throughout the duration of the video.
Synchronization: Integrate manual corrections into the global tracking dataset so that all data reflect the changes made.
Model Updates: If applicable, use the corrected data to retrain the tracking models to improve their accuracy.
> Interventions Documentation:
Correction Reports: Create documents detailing what corrections were made, to which frames, and for which players, including the reasons for the adjustments.
Feedback for Improvements: Use insights from manual review to propose enhancements to the automated tracking processes.
Backup of Corrected Data: Archive the corrected versions of videos and datasets in an organized and secure format for future reference and regulatory compliance.
This process provides a comprehensive analysis of individual and tactical performance, as well as a comparison of performances with historical data to enable sports coaches to make better decisions.
ANALYSIS OF INDIVIDUAL PLAYER PERFORMANCE
> Statistical Analysis:
Creation of Metrics: Performance indices such as successful pass percentage, shot/goal ratio, etc.
Visualization: Charts and tables to represent individual performances (e.g., heatmaps of positioning, speed graphs).
> Evaluation:
Benchmarking: Comparison of player performances with established norms or team averages.
Practical Insights: Specific recommendations for improvement based on analyses.
ANALYSIS OF TEAM TACTICAL PERFORMANCE
> Analysis Setup:
Formation and Strategy: Documentation of tactical formations used and game strategies planned by the team.
Match Data Collection: Tracking of players, collective movements, pressure zones, and transitions.
> Tactical Visualization and Interaction Analysis:
Heat Maps: Generation of heatmaps to highlight areas where game activity is most dense.
Movement Graphs: Visualizations of fluidity and passing patterns among players.
Pass Networks: Study of player interactions on the field.
Tactical Efficiency: Evaluation of game strategies vs. opponent performances.
Modification Suggestions: Proposals for tactical adjustments based on analysis results.
BENCHMARK OF PERFORMANCE VS HISTORICAL DATA
> Comparative Analysis:
Data Compilation: Database of past performance statistics.
Temporal Trends: Identification of trends in player and team performances over time.
Industry Benchmarks: Comparison of current performances with best practices or leaders in the sports field.
> Visualization, Reporting, and Insights:
Dynamic Dashboards: Interactive dashboards to visualize comparisons and trends.
Detailed Reports: Summary report of findings, comparisons, and recommendations.
Long-Term Planning: Use insights from historical analysis to guide long-term team development strategies.
Adaptation to Sport Evolutions: Adjustment of tactics and training based on changes in standards and performances in sports.
