Classification of cricket videos using finite state machines

Ellappan, V.; Rajkumar, R.

doi:10.1504/ijitm.2021.114156

Cited by 2 publications

(2 citation statements)

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“…Group behavior can be identified by analyzing the behavior of individuals and the interaction information between individuals. Individual interactions in a cluster can be represented by individual representations, and analysis of the relationships between individual behaviors can be modeled by modeling the relationships between individual representations [ 16 ]. It is necessary to extract the appearance representation of individuals, to establish the relationship between individual interaction and individual representation, to analyze the behavior of individuals and to analyze the model of group behavior and individual interaction, and to obtain the group behavior by analyzing individual representation, individual behavior, and interaction between individuals.…”

Section: Video Content Analysis Of High-level Semantic Recognition Model Sports Under Engineering Managementmentioning

confidence: 99%

Video Content Analysis of Human Sports under Engineering Management Incorporating High-Level Semantic Recognition Models

Ruan

2022

Computational Intelligence and Neuroscience

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In this paper, a high-level semantic recognition model is used to parse the video content of human sports under engineering management, and the stream shape of the previous layer is embedded in the convolutional operation of the next layer, so that each layer of the convolutional neural network can effectively maintain the stream structure of the previous layer, thus obtaining a video image feature representation that can reflect the image nearest neighbor relationship and association features. The method is applied to image classification, and the experimental results show that the method can extract image features more effectively, thus improving the accuracy of feature classification. Since fine-grained actions usually share a very high similarity in phenotypes and motion patterns, with only minor differences in local regions, inspired by the human visual system, this paper proposes integrating visual attention mechanisms into the fine-grained action feature extraction process to extract features for cues. Taking the problem as the guide, we formulate the athlete’s tacit knowledge management strategy and select the distinctive freestyle aerial skills national team as the object of empirical analysis, compose a more scientific and organization-specific tacit knowledge management program, exert influence on the members in the implementation, and revise to form a tacit knowledge management implementation program with certain promotion value. Group behavior can be identified by analyzing the behavior of individuals and the interaction information between individuals. Individual interactions in a group can be represented by individual representations, and the relationship between individual behaviors can be analyzed by modeling the relationship between individual representations. The performance improvement of the method on mismatched datasets is comparable between the long-short time network based on temporal information and the language recognition method with high-level semantic embedding vectors, with the two methods improving about 12.6% and 23.0%, respectively, compared with the method using the original model and with the i-vector baseline system based on the support vector machine classification method with radial basis functions, with performance improvements about 10.10% and 10.88%, respectively.

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Section: Video Content Analysis Of High-level Semantic Recognition Model Sports Under Engineering Managementmentioning

confidence: 99%

Video Content Analysis of Human Sports under Engineering Management Incorporating High-Level Semantic Recognition Models

Ruan

2022

Computational Intelligence and Neuroscience

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“…This information can include statistics on a player's performance in a particular game or tournament, as well as biographical information, such as their age, height, and weight [10][11]. Cricket is a sport that is particularly well-suited to the application of face recognition technology [12]. With so many players to keep track of, it can be difficult for fans to know who is who on the field.…”

Section: Introductionmentioning

confidence: 99%

Automatic Player Face Detection and Recognition for Players in Cricket Games

Haq,

Sethi,

Ahmad

et al. 2024

IEEE Access

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In this Paper, we create an augmented reality cricket broadcasting application that uses player recognition and automatic detection during play to display player personal data. The system uses AdaBoost to detect player and player face and use PAL based face recognition model to recognize the faces of players on the field. The system is trained on a large dataset of cricket game footage and achieves high accuracy in detecting and recognizing players' faces even with several conditions such as occlusion, non-uniform illumination and pose variation. The system has the potential to enhance the viewing experience of cricket games by providing real-time player identification and statistics. The system can also be used in other sports to provide similar benefits. The paper discusses the system's methodology, results, and implications for the future of sports broadcasting. Overall, the system provides a promising solution for automatic player face detection and recognition in sports broadcasting.

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Classification of cricket videos using finite state machines

Cited by 2 publications

References 0 publications

Video Content Analysis of Human Sports under Engineering Management Incorporating High-Level Semantic Recognition Models

Video Content Analysis of Human Sports under Engineering Management Incorporating High-Level Semantic Recognition Models

Automatic Player Face Detection and Recognition for Players in Cricket Games

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