We developed a solution to detect and track athletes in marathon videos using face and bib number recognition. The system runs on a simple intel i7 machine without a GPU and achieves near real-time performance at 7 fps. Our AI engine resulted in a 97% detection and tracking accuracy, leading to the creation of personalized video clips for registered participants. The client’s productivity increased by 300% with this solution. The technology stack includes Python, Yolo, OpenCV, and deep learning models.
Business Challenge:
The client installs his cameras at various location of the marathon track and records video. A few participants registers with the client. The use case is to detect and track automatically the registered athletes from the marathon’s complete race videos and clip out personalized short videos at each spot the camera is installed.
Solution:
We developed and optimized a solution that performed the automatic detection and tracking of athletes by face and their bib number both available on front of the shirt. The system is deployed on a simple intel i7 machine with out GPU that performs the said task in near real time at 7 fps.
AI Engine Results:
We achieved 97% detection and tracking results to clip out small videos of athletes.
Results:
An app that churns out the personalized video clips of the registered participants in the marathon.
Client Success:
The automatic personalized video extraction increased the productivity of the client by 300%.
Technology Stack:
Language: Python
Technologies: Yolo, OpenCV, Deep Learning Models
Photo Courtesy: olympics.com