A novel recurrent hybrid network for feature fusion in action recognition

Action recognition in video is one of the most important and challenging tasks in computer vision. How to efficiently combine the spatial-temporal information to represent video plays a crucial role for action recognition. In this paper, a recurrent hybrid network architecture is designed for action recognition by fusing multi-source features: a two-stream CNNs for learning semantic features, a two-stream single-layer LSTM for learning long-term temporal feature, and an Improved Dense Trajectories (IDT) stream for learning short-term temporal motion feature. In order to mitigate the overfitting issue on small-scale dataset, a video data augmentation method is used to increase the amount of training data, as well as a two-step training strategy is adopted to train our recurrent hybrid network. Experiment results on two challenging datasets UCF-101 and HMDB-51 demonstrate that the proposed method can reach the state-of-the-art performance.