Spatial Attention Adapted to a LSTM Architecture with Frame Selection 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. In this work we propose an attention mechanism adapted to a CNN--LSTM base architecture. 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 the evaluation metric, obtaining $57.3\%$ and $90.4\% $ for HMDB-51 and UCF-101 respectively.