Sport Analysis Machine Learning

#1

Hey i have a question so i wanted to use python + machine leaning to cut a football highlight video into different sub-categories like shots,goals,fouls

#2

Hi, Ayodele,

It should be a pretty interesting task, but you didn’t write the question.

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#3

My question was could you give me pointers to make the software I was thinking that I can use computer vision to give each player a skeleton and track their movements

#4

I think the best starting point would be a course on computer vision, and the first library that comes to mind when we talk about computer vision is opencv.

I did a quick search and found this one, I haven’t tried it but it looks promising:

#6

To provide more detail on my project i made a visual analysis to explain how each player in each video will given artificial skeleton to tell the machine learning model if the player is making a shot

#8

Hi! There are two common approaches for performing such a task.

The first one is to use pipeline training (you are doing it now), i.e. split your task to few subtasks and train models for each subtask: detect the ball, players, estimate their poses and using all these data classify (by another one model) if the player is making a shot or just running. This is the more traditional approach.

The second one is end-to-end learning: you just feed your model with data and resulting data (i.e. is this video frame contains a shot or not), and if your dataset and model’s hypothesis space are large enough, you’ll be able to solve your task using only one model.

As far as I know, tasks related to classifying sports activities are already able to be solved by the end-to-end approach. As a starting point, I recommend you to investigate these articles: (see the post below)

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#9

action recognition in videos, skeleton-based action recognition. Good luck!

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#10

Thanks if you have more information please contact me

#11

Hello Guys i want to announce that this project is still up and going and i really need more of your ideas to assist me, so far i have gotten two amazing input and i will be glad to receive more input to use the best of my knowledge alongside yours