Action recognition in videos involves analyzing spatial and temporal information. Start by extracting frames from the video and preprocessing them, such as resizing and normalizing.
Use models like 3D convolutional neural networks (3D-CNNs) or Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units to capture temporal dynamics. Alternatively, pre-trained architectures like I3D or SlowFast networks are effective for this task.
Train the model on labeled video datasets, such as UCF101 or Kinetics, and evaluate its performance. Post-training, the model can classify actions in real-time or batch-process videos for tasks like surveillance or sports analysis.