Understanding human action from the visual data is an important computer vision application for video surveillance, sports player performance analysis, and many IoT applications. The traditional approaches for action recognition used hand-crafted visual and temporal features for classifying
specific actions. In this paper, we followed the standard deep learning framework for action recognition but introduced channel and spatial attention module sequentially in the network. In a nutshell, our network consists of four main components. First, the input frames are given to a pre-trained
CNN for extracting the visual features and the visual features are passed through the attention module. The transformed features maps are given to the bi-directional LSTM network that exploits the temporal dependency among the frames for the underlying action in the scene. The output of bi-direction
LSTM is given to a fully connected layer with a softmax classifier that assigns the probabilities to the actions of the subject in the scene. In addition to cross-entropy loss, the marginal loss function is used that penalizes the network for the inter action classes and complimenting the
network for the intra action variations. The network is trained and validated on a tennis dataset and in total six tennis players' actions are focused. The network is evaluated on standard performance metrics (precision, recall) promising results are achieved.