The development of information technologies has given the sports industry new opportunities to collect and process data. Today, software solutions, such as computer vision, provide coaches with high-quality analyzed information and allow them to make better decisions. How can the use of smart systems improve sports results?
The Exposit team worked on a computer vision system designed for Meteor football schools. The developed soft-and-hardware solution allows users to track the movement of football players and balls on the field using cameras and use the collected data to create individual training programs.
Initially, the client monitored and kept records on all the progress and training of students manually. Given that this data pool is constantly growing and needs a lot of resources to analyze, we needed to create a solution that could collect and process various types of data. This way, our client could create individual training programs and increase the involvement of students’ parents using the exact statistics and measurements.
The development process
During the development, we took into account such criteria as cost optimization in terms of equipment, processes automation, and a large amount of video data: the client needs to process more than 150 football grounds of various sizes, coverage, layout, and lighting at the same time.
Our team reviewed the existing options for sports games analytics to create a solution that meets all the requirements:
- Wearable tracking systems that involve the use of budget-friendly individual GPS trackers to build analytics but do not provide high-accuracy results. Such systems are used to collect and process data such as speed, respiration, number of jerks and accelerations, etc.
- Optical tracking systems that include the installation of expensive high-tech cameras around the entire perimeter of the football field, as well as a separate paid subscription to the manual annotation of key match events. Such systems provide greater accuracy and allow you to describe the tactical component of the match but do not contain accurate individual indicators of the players.
During the research, we found that wearable and optical tracking systems are often used together to achieve more accurate football analytics. Thus, we needed to create a solution that combines the capabilities of wearable and optical systems to collect and process various types of data without using expensive equipment.
This solution turned out to be a computer vision system. Since object detection and tracking is a standard task for machine learning algorithms, we suggested that it is also possible to train a neural network to understand actions such as dribbling, passing, intercepting, jumping, tackling, etc.
To test the effectiveness of this solution, our team developed a prototype of football analytics based on computer vision technologies, as well as an operating algorithm – from setting up / calibrating cameras and ground marking to receiving a statistical report.
The prototype can detect the player’s position on the football field during a match or training session and identify a specific player using the reference histograms. Histograms graphically illustrate the number of pixels at each color intensity level creating a unique graph for each player. The result of the system is a file containing data on the coordinates of the players.
Football analytics is a computer vision system that allows users to easily gather data during training sessions and create individual training programs based on the information collected. We are happy to participate in the development of this platform that helps football players improve their results and be better at competitions.
Dmitry Kibkalo, Co-Founder of the Meteor Football Schools, was pleased with the results and left his positive feedback for the services provided on Clutch and DesignRush.
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