SGRNN-AM and HRF-DBN: a hybrid machine learning model for cricket video summarization

Shingrakhia, Hansa; Patel, Hetal

doi:10.1007/s00371-021-02111-8

Cited by 22 publications

(9 citation statements)

References 35 publications

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“…Pan et al (26) have proposed a sensor based approach for sports referee training. Several recent studies have placed emphasis on enhancing the detection of cricket events (27,28) and the summarization of cricket videos (29,30). Nandyal and Kattimani (31) prepared a new dataset named SNWOLF, designed specifically to identify umpire poses in cricket matches.…”

Section: Related Workmentioning

confidence: 99%

Umpire’s Signal Recognition in Cricket Using an Attention based DC-GRU Network

Dey,

Biswas,

Abualigah

2024

IJE

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Computer vision has extensive applications in various sports domains, and cricket, a complex game with different event types, is no exception. Recognizing umpire signals during cricket matches is essential for fair and accurate decision-making in gameplay. This paper presents the Cricket Umpire Action Video dataset (CUAVd), a novel dataset designed for detecting umpire postures in cricket matches. As the umpire possesses the power to make crucial judgments concerning incidents that occur on the field, this dataset aims to contribute to the advancement of automated systems for umpire recognition in cricket. The proposed Attention-based Deep Convolutional GRU Network accurately detects and classifies various umpire signal actions in video sequences. The method achieved remarkable results on our prepared CUAVd dataset and publicly available datasets, namely HMDB51, Youtube Actions, and UCF101. The DC-GRU Attention model demonstrated its effectiveness in capturing temporal dependencies and accurately recognizing umpire signal actions. Compared to other advanced models like traditional CNN architectures, CNN-LSTM with Attention, and the 3DCNN+GRU model, the proposed model consistently outperformed them in recognizing umpire signal actions. It achieved a high validation accuracy of 94.38% in classifying umpire signal videos correctly. The paper also evaluated the models using performance metrics like F1-Measure and Confusion Matrix, confirming their effectiveness in recognizing umpire signal actions. The suggested model has practical applications in real-life situations such as sports analysis, referee training, and automated referee assistance systems where precise identification of umpire signals in videos is vital.

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Section: Related Workmentioning

confidence: 99%

Umpire’s Signal Recognition in Cricket Using an Attention based DC-GRU Network

Dey,

Biswas,

Abualigah

2024

IJE

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“…The performance analysis and outcome projection together with match data from 2015 to 2020 were taken into consideration. A hybrid machine learning strategy is suggested in this study by Hansa Shingrakhia et al ( 61 ) to summarise cricket videos. It examines elements based on excitement, objects, and events to identify significant moments in the cricket video's-AM is used for Audio analysis, HRF-DBN is used for classifying the sequences of each interesting clip.…”

Section: Literature Reviewmentioning

confidence: 99%

Information communication and technology in sports: a meticulous review

Reyaz

Ahamad

Naseem

et al. 2023

Front. Sports Act. Living

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IntroductionSports of all kinds even though have an alluring property of keeping their onlookers stuck to their place, the introduction of Technology, however, revolutionized it all together. Not only in legal sports but also the training and teaching methods have been reformed. The use of Information Communication and Technology (ICT) based technologies [Convolutional Neural Networks (CNN), Hawkeye, Computer vision, Artificial intelligence, etc.] has moderately increased the interactive nature of sports. Employing ICT-driven technologies have continuously been increasing performance levels due to which high effective performance levels have been achieved. In addition to offering information to the users, it also acts as a means for connecting and interacting with the remaining world. In this article, we provide a review of the studies considering the developments and impact of employing ICT technology on sports, especially cricket. The study has focussed on domain-specific developments in cricket sports: developments in the batting domain, bowling domain, and wicketkeeping as well.MethodsFor the study, the analysis has been done following the PRISMA guidelines.ResultsThe study found that even though the researchers have done justifiable work in employing technology in sports as a whole but the domain-specific contribution in sports like cricket is not at the level as is need of the hour. In addition to the mentioned domains in the study, the research should gain speed in other domains like domain-specific Talent Identification for both genders, different age groups, diverse sports, etc.Discussionundoubtedly, the sports domain is employing technology at a vast level but a few domains like sports talent identification especially related to the most famous games like cricket require an immediate and intense focus of the researchers. Since this domain is still carrying out a traditional coach-oriented approach. There is an acute need to revolutionize the domain by incorporating modern technologies into it.

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“…The motion amplitude was used to remove redundant frames from long videos and the text questions were matched with the answers generated from the trained semantic model. Similarly, a hybrid model based on machine learning for the summarization of cricket videos was proposed by [36].…”

Section: B Video Summarization Techniquesmentioning

confidence: 99%

Real-Time Event-Driven Road Traffic Monitoring System Using CCTV Video Analytics

Tahir,

Qiao,

Kanwal

et al. 2023

IEEE Access

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Closed-circuit television (CCTV) systems have become pivotal tools in modern urban surveillance and traffic management, contributing significantly to road safety and security. This paper introduces an effective solution that capitalizes on CCTV video analytics and an event-driven framework to provide realtime updates on road traffic events, enhancing road safety. Furthermore, this system minimizes the storage requirements for visual data while retaining crucial details related to road traffic events. To achieve this, a two-step approach is employed: (1) training a Deep Convolutional Neural Network (DCNN) model using synthetic data for the classification of road traffic (accident) events and (2) generating video summaries for the classified events. Privacy laws make it challenging to obtain extensive real-world traffic data from open-source datasets, and this challenge is addressed by creating a customised synthetic visual dataset for training. The evaluation of the synthetically trained DCNN model is conducted on ten real-time videos under varying environmental conditions, yielding an average accuracy of 82.3% for accident classification (ranging from 56.7% to 100%). The test video related to the night scene had the lowest accuracy at 56.7% because there was a lack of synthetic data for night scenes. Furthermore, five experimental videos were summarized through the proposed system, resulting in a notable 23.1% reduction in the duration of the original fulllength videos. Overall, this proposed system holds significant promise for event-based training of intelligent vehicles in Intelligent Transport Systems (ITS), facilitating rapid responses to road traffic incidents and the development of advanced context-aware systems.

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SGRNN-AM and HRF-DBN: a hybrid machine learning model for cricket video summarization

Cited by 22 publications

References 35 publications

Umpire’s Signal Recognition in Cricket Using an Attention based DC-GRU Network

Umpire’s Signal Recognition in Cricket Using an Attention based DC-GRU Network

Information communication and technology in sports: a meticulous review

Real-Time Event-Driven Road Traffic Monitoring System Using CCTV Video Analytics

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