How do I implement a CNN-LSTM hybrid model for video classification tasks

Question

Can you tell me How do I implement a CNN-LSTM hybrid model for video classification tasks?

Answer 1

To implement a CNN-LSTM hybrid model for video classification, use CNN layers (for spatial feature extraction from frames) and LSTM layers (for temporal sequence modeling), then combine both to classify video sequences.

Here is the code snippet given below:

In the above code we are using the following techniques:

• CNN (TimeDistributed(CNN)) for Frame Feature Extraction:

  • Extracts spatial features from each frame using a shared CNN.

• LSTM for Sequence Learning (LSTM(64)):

  • Models temporal dependencies across frames.

• Uses TimeDistributed() Wrapper:

  • Ensures the CNN is applied independently to each frame.

• Fully Connected Layer for Video Classification (Dense(num_classes)):

  • Predicts video class labels based on combined CNN-LSTM features.

• Scalable for Different Video Datasets:

  • Works with datasets like UCF101, Kinetics, Sports-1M.

Hence, CNN extracts spatial features from video frames, while LSTM models temporal dependencies, making the CNN-LSTM hybrid model ideal for video classification tasks.