pancakeawesome / ran_two_stream_to_recognize_drives

using Recurrent attention network using spatial-temporal relations for action recognition in driving

Action Recognition using Visual Attention

We propose a soft attention based model for the task of action recognition in videos. We use multi-layered Recurrent Neural Networks (RNNs) with Long-Short Term Memory (LSTM) units which are deep both spatially and temporally. Our model learns to focus selectively on parts of the video frames and classifies videos after taking a few glimpses. The model essentially learns which parts in the frames are relevant for the task at hand and attaches higher importance to them. We evaluate the model on UCF-11 (YouTube Action), HMDB-51 and Hollywood2 datasets and analyze how the model focuses its attention depending on the scene and the action being performed.

Dependencies

• Python 2.7
• NumPy
• scikit learn
• skimage
• Theano
• h5py