Towards an Attention Mechanism LSTM Framework for Human Action Recognition in Videos

Action recognition in videos is currently a topic of interest in the area of computer vision, due to potential applications such as: multimedia indexing, surveillance in public spaces, among others. Attention mechanisms have become a very important concept within deep learning approach, their operation tries to imitate the visual capacity of people that allows them to focus their attention on relevant parts of a scene to extract important information. In this paper we propose a soft attention mechanism adapted to a base CNN–LSTM architecture. First, a VGG16 convolutional neural network extracts the features from the input video. Then an LSTM classifies the video into a particular class. To carry out the training and testing phases, we used the HMDB-51 and UCF-101 datasets. We evaluate the performance of our system using accuracy as an evaluation metric, obtaining 40.7% (base approach), 51.2% (with attention) for HMDB-51 and 75.8% (base approach), 87.2% (with attention) for UCF-101.