# Feature Fusion Graph Consecutive-Attention Network for Skeleton-Based Tennis Action Recognition
## 1 Introduction  
Human Action Recognition (HAR) is currently widely applied to recognize complex movements of athletes, including accuracy prediction or to detect objects in images or videos [1]. Due to the large amount of data gathered via YouTube, traffic cameras, social media, and motion capture systems, as well as stored in publicly available datasets, this area of Computer Vision (CV) possesses sufficient sources to apply deep learning (DL) models in order to enhance athlete's performance. These modern techniques may also encourage people to learn and master sports skills. Recognition of sports movements plays a pivotal role in monitoring athletes during training, matches, and competitions. Another aspect of HAR is to verify how sport movements are performed. For these tasks,the recognition of specific moves is essential. The development of vision systems allows one to obtain increasingly accurate data, which in the case of sport analysis is of a very significant importance.  
The main challenges of HAR are faced in detecting sport actions and activities, which are further applied for monitoring a player's performance. Recently studies have focused on fusion of the skeleton structure of sensor-based data, together with other representations like joint position and velocities [2], kinematics data [3], and trajectories [4; 5; 6; 7]. These approaches provide topological relations that are further utilized in the recognition process. The fusion of motion capture data provides a better representation of this kind of data. Thus, this study focuses on discussing the fundamental issues of high-accuracy tennis movement recognition. DL models have difficulties in accurately identifying both short-term and long-term dynamic characteristics, as well as in extracting joint information at different scales from motion capture data, especially for very dynamic movements. In response to the above challenges, the authors proposed a new model for recognizing the main tennis movements. The main contributions are as follows:  
* Motion capture datasets containing basic tennis strokes were selected, such as THETIS, Tennis-Mocap, and 3 DTennisDS. The first contains data recorded with a markerless system, while the latter two used optical, retro-reflective systems. The data contain the positions of participants performing individual tennis movements. The 3 DTennisDS dataset additionally contains the position of the tennis racket.
* Participant data obtained from the three datasets were unified and simplified to the THETIS dataset, in order to create similar input data for the model.
* The following tennis movements were chosen to recognize: forehand, backhand, volley forehand, and volley backhand.
* The authors propose a new model to recognize the abovementioned tennis moves. The Feature Fusion Graph Consecutive-Attention Network (FFGCAN) was created, which incorporates seven basic blocks that are combined with two types of module: Adaptive Consecutive Attention Module, and Graph Self-Attention. Additionally, a temporal convolutional network, as a part of the network, models short-term temporal dependencies.
* The FFGCAN was verified utilizing the following metrics: accuracy, precision, recall, F1-score, and confusion matrices. The training process toward accurate prediction was analyzed.
* In order to visualize which features are the most essential for tennis movement recognition, Gradient-weighted Class Activation Mapping (Grad-CAM) was applied. This CV technique indicated the most relevant data for making predictions.