A popular research topic in computer vision and multimedia analysis is human activity recognition . High-difficulty action recognition technology in basketball is mainly to identify and analyze the physical behavior of basketball players. Video recognition is an important guarantee for improving the level of basketball training. Traditional sports target recognition is limited by technology and injury, and cannot achieve the desired effect. Basketball offence is a crucial part of the game that involves cutting, dribbling, passing, screening, and shooting. Athletes and coaches alike benefit greatly from collecting and evaluating posture data. The current basketball action detection system is inefficient and has a high error rate. A typical human action recognition pipeline has three steps: action object detection, feature extraction, and recognition. Single-feature recognition is often incapable of delivering stable and precise performance. Multi-model feature fusion improves recognition accuracy [21, 22].
Traditional basketball education techniques can be reiterated, affecting severe basketball teaching effectiveness along with the gaining of technical requirements. Grounded on this issue, the basketball coaching reproduction structure is constructed using augmented reality invention. The proposed study  utilizes fundamental concepts, features, virtual reality strategies, literature and information approaches to describe the varieties of role play in basketball training. In addition, the study examines the application programs of basketball theory teaching, technical learning, strategic education, and institutional competitions that offer scientific principles for upcoming basketball teaching modification. Decision-making has an important role in basketball offenses. Tsai et al.  have presented an article that proposes a motion-aware offensive decision-making coaching system for basketball using virtual reality (VR) and artificial intelligence technologies. Novices wearing a head-mounted display (HMD) as well as an action capture outfit are prepared by instinctively cooperating with the VR system and obtaining judgment recommendations when a negative one is made. Furthermore, the research diverse the coaching mediums and approaches to make an immersive coaching atmosphere during the coaching phase and assessed the coaching efficiency. Experimental outcomes show that the coaching situation affects the preparation in terms of judgment time. Nowadays most researches concentrate on coarsely-grained motions, whereas fine-grained motion recognition is rarely focused which is of key significance in numerous uses like video retrieval. The article  issue a challenging dataset by interpreting the fine-grained motions in basketball game videos. A benchmark assessment of the state-of-the-art methods for motion recognition is also offered in the presented dataset. To locate the most instructive areas and mine more discriminative features for fine-grained motion recognition, the study presented a method by incorporating the NTS-Net into two-stream network. Experimental outcomes indicate that the presented method performs very well as compared to the existing method.
The study presents an innovative method to fuse the global and local gesture pattern separation as well as vital visual information for semantic recognition in basketball videos. For group activity recognition and for success or failure estimation, these both KVI and MPs charters were mined. At first, the study presented an algorithm to predict the global actions from mixed actions grounded on the intrinsic property of camera adjustments. While the local actions can be achieved from the mixed and global actions. Secondly, a two-stream 3D CNN outline is used for group task recognition and thirdly, the basket is identified as well as its exterior features were mined via a CNN framework. The features are used to forecast success or failure. Experimental outcomes indicate that the presented approach achieves superior performance . By using triple Kinect sensors, Yao et al.  have presented a novel human motion recognition system. A weighted incorporation approach is utilized to incorporate the multi-view skeleton data. The study utilizes joint velocities, angles, as well as angular velocities as features to identify human motion. For capturing the temporal characteristic of human motion, the study utilizes the average of joint velocities, average angles, along with angular velocities as temporal features. In addition, the proposed study constructed the classifier grounded on part-aware lengthy short-term memory (PLSTM) to determine human action. The feasibility of the presented system has been proved by the experimental outcomes.
This study used a decision support system to prioritize basketball skill action recognition. The super decision tool was employed in the trial process. The process was initially divided into three parts, the goal which is the skill action recognition, the criteria which are cutting, dribbling, passing, screening, and shooting, and the available six alternatives (A1, A2,…A6). Figure 1 depicts basketball court and the process of plotting the super decision tool is shown in Fig. 2. Those are the five primary offensive skills. Each player must build a good basis for each skill set, even if some players excel in certain skills. The following criteria have been followed.
The defense cannot stop two activities at the same moment in basketball. The defense will focus on on-ball actions like dribble penetration, allowing the offence to cut off-ball.
Dribbling is another key technique when a player with the ball bounces it up and down on the floor with one hand. For example, during a live turnover fast break opportunity with just one player to beat, dribbling allows players to advance the ball towards the hoop.
The passing game is very vital in basketball offence. The offence will likely stagnate if players cannot make precise and/or timely passes. The offensive team's ability to generate high percentage shots will be severely hampered.
Basketball players should master and use the fundamental technique of screening. Basketball screens can be on-ball screens used in pick and roll action or off-ball screens like the down screen or cross screen.
Shooting is undoubtedly the most vital skill in any basketball offence since without it, a team cannot score points.
After the process of plotting, the process of pair wise comparisons were done in the software and Fig. 3 shows the process of comparison of criteria.
The same process was done for rest of the available criteria. Figure 4 describe the process of comparison for available alternative 1. The rest of the process of pair wise comparison of alternatives was done in the same manner.
Once the process of comparisons of all the criteria alternatives were done then the results were summarized into unweighted matrix which is shown in Table 1.
Table 1. Unweighted matrix
The unweighted matrix was normalized further to obtain the weighted matrix which is shown in table 2.
Table 2. Weighted matrix