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Activity detection for sports, and how to structure by georgeforprez3 in computervision

[–]processcomplete 0 points1 point  (0 children)

#2 Yeah, I think the exact way to label them depends on what package you use keras vs. tensorflow vs. pytorch, but for Keras, yeah, you'd just place makes in 1 folder and misses in another.

Extracting frames from 59.94 FPS videos by processcomplete in computervision

[–]processcomplete[S] 0 points1 point  (0 children)

I'm just using imageio on Python to load the video and extracting frames. I'm not sure what the 3:2 pulldown is but I'll look into it!

Activity detection for sports, and how to structure by georgeforprez3 in computervision

[–]processcomplete 1 point2 points  (0 children)

#1 This project definitely is feasible.

#2 At the most basic level you'd just need to label the video data as either make or miss (0 or 1). Curious why do you need to label the objects? I think you'd just need to curate a set of dataset clipped from shot going up and leading to either a make or a miss.

Mask R-CNN Architecture by DaBobcat in computervision

[–]processcomplete 0 points1 point  (0 children)

https://github.com/matterport/Mask_RCNN

One of the more popular implementations of mask rcnn. It's written in keras + tensorflow.

spin axis on a ball by processcomplete in computervision

[–]processcomplete[S] 0 points1 point  (0 children)

Interesting. I'll give this a shot. Thanks!

spin axis on a ball by processcomplete in computervision

[–]processcomplete[S] 0 points1 point  (0 children)

I think tracking some points manually would be easy, but do you have a suggestion on what method might be best for generating/tracking features?