In the world, cycling is the sport with the highest user demand. Skilled road cyclists riding on the world tour pay particular attention to their choice of equipment, and the position of a rider on the bike makes a huge contribution to the success of the competitor by making the rider more aerodynamic and relaxed. Riders will optimize their riding position in a wind tunnel to suit the evolving frame geometries from year to year and with various suppliers, minimizing the aerodynamic drag. From race to race, bikes would get disassembled to save space in transport. In comparison, as riders want to change components-saddles, handlebars for various courses, team technicians must make those adjustments in a repair course at a mechanic's truck-parking lot of a hotel or event beginning location-quickly and with fewer complex, easy-to-carry equipment. Equipment available on the market is bulky and time-intensive to set up. This research proposed to use image processing techniques to accurately measure key aspects of bicycle geometry using a mobile phone camera to help race mechanics in replicating riders’ positions on a bicycle. For applying image processing techniques, OpenCV-python was being used. Finally improved the system by adding new features. The technology used machine learning and computer vision to automate the system. This system was able to calibrate the key measurements. And the system can use a phone and reduce the complexity and return the best results..
- Frontend -> Angular, OpenCV.js, TypeScript
- Backend -> Python, Flask, OpenCV, Shi-Tomasi