: Use NumPy or Pandas to store and concatenate the resulting feature vectors.

You can implement this using standard libraries like or Keras . A typical pipeline involves: Loading the video : Use OpenCV or PyAV .

:Choose a pre-trained model (backbone) based on your specific goal:

:Instead of using the final classification layer, "deep features" are extracted from the last Fully Connected (FC) layer or a late Global Average Pooling (GAP) layer. This provides a high-dimensional vector (e.g., 1,024 or 2,048 elements) representing the frame's content.

: Use PyTorch Torchvision or Keras Applications to load pre-trained models.

: Use VGG-16 , ResNet-50 , or EfficientNet to capture general visual hierarchies.