Recent developments in video analysis of sports and computer vision techniques have achieved significant improvements to enable a variety of critical operations. To provide enhanced information, such as detailed complex analysis in sports like soccer, basketball, cricket, badminton, etc., studies have focused mainly on computer vision techniques employed to carry out different tasks. This paper presents a comprehensive review of sports video analysis for various applications: high-level analysis such as detection and classification of players, tracking player or ball in sports and predicting the trajectories of player or ball, recognizing the team‟s strategies, classifying various events in sports. The paper further discusses published works in a variety of application-specific tasks related to sports and the present researcher‟s views regarding them. Since there is a wide research scope in sports for deploying computer vision techniques in various sports, some of the publicly available datasets related to a particular sport have been provided. This work reviews a detailed discussion on some of the artificial intelligence (AI) applications in sports vision, GPU-based work stations, and embedded platforms. Finally, this review identifies the research directions, probable challenges, and future trends in the area of visual recognition in sports.

/pdf/a-comprehensive-review-of-computer-vision-in-sports-open-2ziakwzp.pdf

A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions

To understand human behaviors, action recognition based on videos is a common approach. Compared with image-based action recognition, videos provide much more information. Reducing the ambiguity of actions and in the last decade, many works focused on datasets, novel models and learning approaches have improved video action recognition to a higher level. However, there are challenges and unsolved problems, in particular in sports analytics where data collection and labeling are more sophisticated, requiring sport professionals to annotate data. In addition, the actions could be extremely fast and it becomes difficult to recognize them. Moreover, in team sports like football and basketball, one action could involve multiple players, and to correctly recognize them, we need to analyse all players, which is relatively complicated. In this paper, we present a survey on video action recognition for sports analytics. We introduce more than ten types of sports, including team sports, such as football, basketball, volleyball, hockey and individual sports, such as figure skating, gymnastics, table tennis, tennis, diving and badminton. Then we compare numerous existing frameworks for sports analysis to present status quo of video action recognition in both team sports and individual sports. Finally, we discuss the challenges and unsolved problems in this area and to facilitate sports analytics, we develop a toolbox using PaddlePaddle, which supports football, basketball, table tennis and figure skating action recognition.

A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications

Recognizing the sport of cricket on the basis of different batting shots can be a significant part of context-based advertisement to users watching cricket, generating sensor-based commentary systems and coaching assistants. Due to the similarity between different batting shots, manual feature extraction from video frames is tedious. This paper proposes a hybrid deep-neural-network architecture for classifying 10 different cricket batting shots from offline videos. We composed a novel dataset, CricShot10, comprising uneven lengths of batting shots and unpredictable illumination conditions. Impelled by the enormous success of deep-learning models, we utilized a convolutional neural network (CNN) for automatic feature extraction, and a gated recurrent unit (GRU) to deal with long temporal dependency. Initially, conventional CNN and dilated CNN-based architectures were developed. Following that, different transfer-learning models were investigated—namely, VGG16, InceptionV3, Xception, and DenseNet169—which freeze all the layers. Experiment results demonstrated that the VGG16–GRU model outperformed the other models by attaining 86% accuracy. We further explored VGG16 and two models were developed, one by freezing all but the final 4 VGG16 layers, and another by freezing all but the final 8 VGG16 layers. On our CricShot10 dataset, these two models were 93% accurate. These results verify the effectiveness of our proposed architecture compared with other methods in terms of accuracy.

https://www.mdpi.com/1424-8220/21/8/2846/pdf

CricShotClassify: An Approach to Classifying Batting Shots from Cricket Videos Using a Convolutional Neural Network and Gated Recurrent Unit

Auxiliary research on difficult aerobics exercise training based on fpga and movement recognition technology

Dynamic 3D image simulation of basketball movement based on embedded system and computer vision

Basketball action recognition based on FPGA and particle image

Due to its abundant reserves and pure water quality, groundwater is an important water resource on the earth, which is widely distributed and easy to use, so it is widely used in industrial production, agricultural production, and domestic drinking. Most urban and rural areas in China use groundwater as drinking water source, and the quality of groundwater is closely related to the life of residents. Indiscriminate use of agricultural fertilizers, wanton discharge of industrial wastewater, and untreated wastewater will cause heavy metal pollution in groundwater. Groundwater is polluted by characteristic pollutants, which makes it difficult to deal with, and the deeper the water layer is, the lower the self-purification capacity will be, threatening the health of residents. In this paper, the groundwater samples of 58 towns in l area were collected as the investigation area, and the contents of fluorine ion and 16 kinds of metals and nonmetals were analyzed and discussed by ICP-MS and other equipment, as well as the characteristics of rural groundwater pollution in dry period. At the same time, the index model fuzzy decision making and single factor of health risk assessment model were used to evaluate the carcinogenic and non-carcinogenic risk. Obesity among Chinese adolescents is also a very worrying problem. The current literature supports many weight loss methods. Exercise weight loss can not only reduce fat, but also improve physical strength. In the long run, it can make us gain synergy. In life, people are aware of the impact of aerobic exercise on weight loss, so they rarely use high-intensity temporary strength for training. According to the research of fuzzy decision making, it is applied to groundwater and aerobic movement, so that it can be effectively improved.

Characteristics of heavy metal pollutants in groundwater based on fuzzy decision making and the effect of aerobic exercise on teenagers

Retraction Note to: Characteristics of heavy metal pollutants in groundwater based on fuzzy decision making and the effect of aerobic exercise on teenagers

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Basketball action recognition based on FPGA and particle image

Characteristics of heavy metal pollutants in groundwater based on fuzzy decision making and the effect of aerobic exercise on teenagers

Retraction Note to: Characteristics of heavy metal pollutants in groundwater based on fuzzy decision making and the effect of aerobic exercise on teenagers