Player segmentation
Player segmentation involves annotating players and their equipment in sports images and videos to help train computer vision models to distinguish athletes from the surrounding scene. These structured datasets support player segmentation, sports scene analysis, and other sports-related AI applications.
- Hockey player tracking
- Baseball player tracking
- Football player tracking
- Volleyball player tracking
Player Tracking
Player tracking annotation labels players, balls, bats, and other relevant objects across video frames to prepare datasets for player tracking and object tracking models. Consistent tracking labels, where supported, help models follow object movement throughout a video sequence.
- Golf player
- Football player
- Tennis player
Action Analysis
Human pose annotation for sports involves labeling skeletal key points on athletes to create training data for human pose estimation and movement analysis. These pose annotations help AI models identify body positions and support the analysis of sports movements, gestures, and actions.
Fitness App
Fitness app data annotation involves labeling body key points and exercise movements to prepare training datasets for AI-assisted fitness applications. These datasets can support exercise recognition and movement analysis when the required labels are included, helping teams develop and evaluate fitness-focused AI models.
Racing Game Analytics
Racing game analytics annotation focuses on labeling relevant objects and visual elements in racing game footage or images to support AI training for game analytics and computer vision applications. The annotation scope can be tailored to the specific game environment, target objects, and model requirements.
Frequently Asked Questions
Sports video annotation services involve labeling players, balls, equipment, and relevant movements across video frames. The labeled datasets can help train and evaluate computer vision models for sports analytics, player tracking, and action recognition.
Sports image annotation labels objects such as athletes, balls, bats, and other equipment in still images. These datasets can support AI model development for object detection, player segmentation, and sports scene analysis.
Player segmentation identifies the pixels belonging to a player in an image or video frame. Player tracking connects a player's detections across multiple frames to represent movement over time. Both techniques can support sports computer vision applications, but they serve different purposes.
Sports annotation requirements may include sports such as football, hockey, baseball, volleyball, golf, and tennis. The supported sports, object categories, and annotation methods should be confirmed based on the project's requirements.
Human pose annotation involves labeling skeletal keypoints, such as relevant joints and body landmarks, on athletes. These annotations can help prepare training datasets for human pose estimation, movement analysis, and sports action recognition models.
Annotators can label body keypoints, poses, and defined actions in sports images or videos according to project guidelines. These labels can be used to prepare datasets for training or evaluating models designed to recognize specific sports movements.
