22 players compete for possession of the ball in football, one of the most popular sports on the planet. The data we gather from football games may help us understand why that is the case even though watching them is an important part of the experience.
My contribution to the subproblem of football analysis describes my experience interpreting television-like video streams of football matches. To find out more, visit baanstepball.com.
The positional and semantic information can be extracted from moving cameras, even though fixed cameras can be positioned throughout a site. This was not possible in real stadiums because of budget restrictions and permission restrictions. Whether you want to stay seated or have a tight budget, there are many ways to process video data.
It was decided (as is the case for a textbook programmer) that we would break up this difficult task into smaller, more manageable chunks.
Consequently, the following divisions have been created:
- The positions of the players are projected onto a two-dimensional space using the camera view (reference estimate and homography estimate).
- Identification of players, balls, and officials (e.g., from where they come) is required.
- The tracking of objects (also called entity tracking) is crucial to the success of my project.
- When a player appears in a frame, does it matter who it is? How important is the player appearing in each frame?
- How do I find out which team a player represents?
After that, we will focus on the specific tasks, such as positioning and semantic analysis.
The fields and entities that are detected serve as the basis for determining the objects in each frame sequence. This type of tracking is used when events are detected nearly consecutively.
Using a similar method, we estimate the positioning of each entity relative to the camera by projecting its position. It is also possible to identify and place each player in a team to determine his performance.
Our process of repeating the video frame by frame is started as soon as the video ends. This is followed by smoothing the data. The backward adjustments are performed in search of similarity in trajectory detection and trajectory paths after the data has been collected frame by frame.
When a frame is fed into the system, the steps that are performed within it are immediately visible.
The first thing one notices whenever they deal with machine learning is how difficult it is to locate good labeled data. For instance, loV3 is one popular object detector.
In the case of pre-trained nets, the results are disappointing because the frame will be cropped. It was more important that the original resolution image be transmitted accurately than quickly, so YOLO was used for transmission. When the ball is near a player or referee, the method can be used to determine where the ball is.