I am working on a project where I want to classify what's currently on a frame.
For example, given a video of a TV recording, you would have classes as: Ads, Show Opening, Show.
I have multiple video files, around 20~30 minutes, at 24 FPS, a 20 min video generates 28800 frames among unbalanced classes (more data points from Show > Ads > Show Opening)
I started testing a
LinearSVC in a single video which provided good results, but adding more videos makes my data set too large, and a can't fit a LinearSVC in batches (I'm using scikit learn)
I'm wondering which steps to take, mostly:
- Discard data points to make my train data-set smaller? Should I discard randomly, or discard similar data points?
- Should I explore other ML classifiers or move to Neural-nets? Are there specific techniques for video processing? Currently I'm treating the video classification as several image classification problems, should I model my problem differently?