Spatiotemporal saliency-based multi-stream networks with attention-aware LSTM for action recognition

Abstract

Human action recognition is a process of labeling video frames with action labels. It is a challenging research topic since the background of videos is usually chaotic, which will reduce the performance of traditional human action recognition methods. In this paper, we propose a novel spatiotemporal saliency-based multi-stream ResNets (STS), which combines three streams (i.e., a spatial stream, a temporal stream and a spatiotemporal saliency stream) for human action recognition. Further, we propose a novel spatiotemporal saliency-based multi-stream ResNets with attention-aware long short-term memory (STS-ALSTM) network. The proposed STS-ALSTM model combines deep convolutional neural network (CNN) feature extractors with three attention-aware LSTMs to capture the temporal long-term dependency relationships between consecutive video frames, optical flow frames or spatiotemporal saliency frames. Experimental results on UCF-101 and HMDB-51 datasets demonstrate that our proposed STS method and STS-ALSTM model obtain competitive performance compared with the state-of-the-art methods.