Inspiration
The inspiration for this project comes from the desire to leverage computer vision technology to improve basketball players' free throw performance. Free throws are an essential aspect of basketball, and even the slightest variation in form or technique can have a significant impact on a player's accuracy and consistency. With the help of computer vision, we can analyze a player's free throw motion in real-time, providing valuable insights that can help them improve their form, consistency, and accuracy. This technology can be especially useful for coaches and players looking for a data-driven approach to improve their performance on the court.
What it does
ShotSense aims to enhance the performance of basketball players in free throw shooting through the use of computer vision technology. By analyzing video footage of a player's free throw, the technology can track the player's body movements, including their posture, arm position, and release point. The resulting data can be used to evaluate the player's technique, identify areas for improvement, and provide personalized feedback. Coaches can use this technology to track their players' progress over time, optimize their training plans, and improve their overall performance on the court. The technology offers a fast and precise way to assess a player's free throw, allowing them to fine-tune their technique and achieve greater accuracy and consistency in their shots.
How we built it
We built the project by leveraging Google's Mediapipe library and their Blazepose model for keypoint data analysis of a basketball player's free throw motion. We developed a custom algorithm to track the body movements and evaluated the angle of the elbow to provide real-time feedback to players on their shooting form.
Challenges we ran into
One of the main challenges we faced was ensuring the accuracy of the keypoint data provided by the Blazepose model. We had to experiment with different camera angles, lighting conditions, and player poses to ensure that the keypoint data was consistent and reliable. Another challenge we faced was developing an algorithm that could accurately track a player's body movements during a free throw motion, despite variations in form and technique. This required extensive testing and tweaking to ensure that the algorithm could handle a wide range of scenarios and provide accurate feedback to players. Despite the challenges we faced, we were able to overcome them through extensive effort and collaboration
Accomplishments that we're proud of
We are proud of developing a cutting-edge tool that leverages computer vision technology to provide real-time feedback to basketball players and coaches. Additionally, we are proud of the accuracy and reliability of our keypoint tracking algorithm. These accomplishments represent a significant advancements in the field of basketball training and performance analysis.
What we learned
We learned the importance of selecting the right machine learning model for the task at hand. Through trial and error, we found that the BlazePose model from Google's MediaPipe library was the most accurate and reliable for tracking keypoint data in real-time. Open source projects are a gold mine of knowledge
What's next for ShotSense
Developing a mobile app or web-based platform to make the tool more accessible to players and coaches. Expanding the tool to analyze other aspects of a player's form, such as their foot placement or body position, to provide a more complete analysis of their free throw technique
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