One Swing, Many Frames: How We Aggregated Video into a Summary with Pixeltable

PixelGolf, an internal project still under development, is designed to provide golfers with detailed swing analysis by aggregating data from swing video frames. The process involves using MediaPipe for per-frame analysis and leveraging Pixeltable's User Defined Aggregator (UDA) for effective data aggregation, which has proven to be the most challenging aspect of the project, rather than pose detection or coaching through large language models. Despite experimenting with various methods like user-defined functions (UDFs) and queries, the team found that using UDA in combination with group_by queries is the most efficient approach for aggregating metrics across multiple frames into a single summary. However, challenges remain in implementing a fully declarative rollup of aggregated data onto a parent table, an aspect the team hopes to improve with future updates to Pixeltable. The blog post shares insights and lessons learned from the development process, emphasizing the importance of choosing the right Pixeltable primitives for different aggregation tasks in sports video analysis.

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