Viewpoint constrained and unconstrained Cricket stroke localization from untrimmed videos

Gupta, Arpan; Muthiah, Sakthi Balan

doi:10.1016/j.imavis.2020.103944

Cited by 11 publications

(3 citation statements)

References 10 publications

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“…In many aspects of cricket as well, computer vision techniques can effectively replace manual analysis. A cricket match has many observable elements including bat-ting shots [114][115][116][117][118][119][120][121], bowling performance [122][123][124][125][126][127], number of runs or score depending on ball movement, detecting and estimating the trajectory of the ball [128], decision making on placement of players' feet [129], outcome classification to generate commentary [130,131], detecting umpire decision [132,133]. Predicting an individual cricketer's performance [134,135] based upon his past record can be critical in team member selection at international competitions.…”

Section: Cricketmentioning

confidence: 99%

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

2022

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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 such as soccer, basketball, cricket, and badminton, 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 players or balls in sports and predicting the trajectories of players or balls, recognizing the team’s strategies, and 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 discussed. This paper reviews detailed discussion on some of the artificial intelligence (AI) applications, GPU-based work-stations and embedded platforms in sports vision. Finally, this review identifies the research directions, probable challenges, and future trends in the area of visual recognition in sports.

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Section: Cricketmentioning

confidence: 99%

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

2022

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“…Consequently, it demands added hardware with increased power consumption. Therefore, this technique is of limited interest, and improvements are expected for achieving optimal and feasible solutions in real-time application scenarios [17,18]. To attain desirable precision, larger antenna arrays must be used.…”

Section: Angle Of Arrivalmentioning

confidence: 99%

Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues

2021

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With exponential growth in the deployment of Internet of Things (IoT) devices, many new innovative and real-life applications are being developed. IoT supports such applications with the help of resource-constrained fixed as well as mobile nodes. These nodes can be placed in anything from vehicles to the human body to smart homes to smart factories. Mobility of the nodes enhances the network coverage and connectivity. One of the crucial requirements in IoT systems is the accurate and fast localization of its nodes with high energy efficiency and low cost. The localization process has several challenges. These challenges keep changing depending on the location and movement of nodes such as outdoor, indoor, with or without obstacles and so on. The performance of localization techniques greatly depends on the scenarios and conditions from which the nodes are traversing. Precise localization of nodes is very much required in many unique applications. Although several localization techniques and algorithms are available, there are still many challenges for the precise and efficient localization of the nodes. This paper classifies and discusses various state-of-the-art techniques proposed for IoT node localization in detail. It includes the different approaches such as centralized, distributed, iterative, ranged based, range free, device-based, device-free and their subtypes. Furthermore, the different performance metrics that can be used for localization, comparison of the different techniques, some prominent applications in smart cities and future directions are also covered.

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“…In this work, we use the dataset of Gupta et al [5] with 562 stroke labels in 26 untrimmed Highlights videos of T20 World Cup 2016. They have dealt with the problem of temporal cricket stroke localization in [6,7]. Gupta et al [5] have used an unsupervised clustering approach on the averaged motion features of the strokes and visualized them for the same dataset.…”

Section: Literature Surveymentioning

confidence: 99%

Cricket Stroke Recognition Using Hard and Soft Assignment Based Bag of Visual Words

Gupta

Karel

Muthiah

2021

Communications in Computer and Information Science

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In this work, we deal with the problem of activity recognition in Cricket telecast videos. We present a supervised approach for recognizing Cricket stroke categories using two variants of Bag of Visual words (BoV) model applied on dense optical flow based motion feature i.e., grid-based flattened vectors and orientation histograms, 3D ResNet extracted features and 2D ResNet extracted spatial features. These globally extracted features, in spite of the noise due to camera motion, give good results on the Cricket strokes dataset having 562 trimmed stroke videos. We independently labeled the strokes based on the direction of stroke play and the direction of camera motion, into five and three categories. respectively, and provide experimental analysis on Hard Assignment (HA) and Soft Assignment (SA) based BoV methods.Our contribution lies in showing the effectiveness of the unordered BoV representation for direction based action recognition. The experimental analysis presented in our work provides an insight into the BoV HA and SA models applied to global frame-level feature descriptors. Moreover, our experiments suggest the orientation histograms to be simple yet effective for direction based recognition tasks. The best accuracy achieved on the Cricket strokes test partition was 85.85% with orientation histograms and 82.08% with grid features, for 3 category and 5 category labels, respectively.

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Viewpoint constrained and unconstrained Cricket stroke localization from untrimmed videos

Cited by 11 publications

References 10 publications

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

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

Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues

Cricket Stroke Recognition Using Hard and Soft Assignment Based Bag of Visual Words

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