科研之友
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摘要
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.
