Using Computer Vision to Assess Bouldering Performance

Bouldering, a form of climbing performed without ropes on small rock formations or indoor walls, emphasizes mastering complex movements rather than reaching a summit. Recognizing the challenge of tracking subtle progress in this sport, Daniel Reiff developed BoulderVision, a computer vision tool that analyzes climbing footage to provide detailed insights into a climber's technique and progress. BoulderVision uses video capture, object detection, image classification, and keypoint detection to transform climbing videos into actionable data, with features such as heatmaps and movement dynamics. It employs Roboflow Workflows, a framework for processing video frames and generating analytics, allowing climbers to visualize and improve their performance. The project combines technical innovation with Reiff's passion for climbing, offering a sophisticated system that can be customized and expanded with features like 3D depth integration and hold type detection. The initiative encourages enthusiasts to explore similar systems for personal interests, contributing to a deeper understanding and enhancement of bouldering techniques.