Dynamic tracking attention model for action recognition
This paper proposes a dynamic tracking attention model (DTAM), which mainly comprises a motion attention mechanism, a convolutional neural network (CNN) and long short-term memory (LSTM), to recognize human action in a video sequence. In the motion attention mechanism, the local dynamic tracking is used to track moving objects in feature domain and global dynamic tracking corrects the motion in the spectral domain. The CNN is utilized to perform feature extraction, while the LSTM is applied to handle sequential information about actions that is extracted from videos. It effectively fetches information between consecutive frames in a video sequence and has an even higher recognition rate than does the CNN-LSTM. Combining the DTAM with the visual attention model, the proposed algorithm has a recognition rate that is 3.6% and 4.5% higher than that of the CNN-LSTMs with and without the visual attention model, respectively.