A Proposed Method for Deep Learning-Based Automatic Tracking with Minimal Training Data for Sports Biomechanics Research

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Background: This technical note proposes a deep learning-based, few-shot automatic key point tracking technique tailored to sports biomechanics research. Methods: The present method facilitates the arbitrary definition of key points on athletes’ bodies or sports equipment. Initially, a limited number of video frames are manually digitized to mark the points of interest. These annotated frames are subsequently used to train a deep learning model that leverages a pre-trained VGG16 network as its backbone and incorporates an additional convolutional head. Feature maps extracted from three intermediate layers of VGG16 are processed by the head network to generate a probability map, highlighting the most likely locations of the key points. Transfer learning is implemented by freezing the backbone weights and training only the head network. By restricting the training data generation to regions surrounding the manually annotated points and training specifically for each video, this approach minimizes training time while maintaining high precision. Conclusions: This technique substantially reduces the time and effort required compared to frame-by-frame manual digitization in various sports settings, and enables customized training tailored to specific analytical needs and video environments.