
AWS enables accurate ball trajectory tracking in sports broadcast videos using deep learning and managed services including SageMaker Ground Truth for labeling, SageMaker Processing for feature engineering, and SageMaker Training and Inference for model deployment without expensive camera equipment.


<div><p>This article describes an end-to-end machine learning solution for tracking ball trajectories in sports broadcast videos using AWS services, without expensive camera equipment.</p><ul><li>Uses TrackNet deep learning architecture to detect tiny, fast-moving balls in broadcast video</li><li>SageMaker Ground Truth labels ball positions frame-by-frame across video footage</li><li>SageMaker Processing job performs feature engineering on consecutive frames</li><li>SageMaker Training job trains TrackNet model combining VGG-16 and DeconvNet networks</li><li>Real-time SageMaker inference endpoint with GPU support detects ball positions</li><li>Application overlays circles on detected ball positions to create trajectory video</li><li>GitHub repository provides working implementation for badminton and other ball sports</li></ul><p>The solution demonstrates how AWS managed ML services enable accurate ball tracking for sports analysis without specialized hardware.</p></div>


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