Video Summarization for Multiple Sports Using Deep Learning

Guntuboina, Chakradhar; Porwal, Aditya; Jain, Preet; Shingrakhia, Hansa

doi:10.5565/rev/elcvia.1286

Cited by 21 publications

(5 citation statements)

References 15 publications

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“…In the existing literature, YOLO is mostly used to summarize sports videos. An automated sports video summarization technique presented by [18], in which YOLO was trained on 1300 images of different scores. A frame was given as input to YOLO after every second, if scoreboard was present, then score was inputted to optical character recognition (OCR) to compare the previous and current score.…”

Section: Video Summarization Through Yolomentioning

confidence: 99%

Privacy Preserved Video Summarization of Road Traffic Events for IoT Smart Cities

et al. 2023

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The purpose of smart surveillance systems for automatic detection of road traffic accidents is to quickly respond to minimize human and financial losses in smart cities. However, along with the self-evident benefits of surveillance applications, privacy protection remains crucial under any circumstances. Hence, to ensure the privacy of sensitive data, European General Data Protection Regulation (EU-GDPR) has come into force. EU-GDPR suggests data minimisation and data protection by design for data collection and storage. Therefore, for a privacy-aware surveillance system, this paper targets the identification of two areas of concern: (1) detection of road traffic events (accidents), and (2) privacy preserved video summarization for the detected events in the surveillance videos. The focus of this research is to categorise the traffic events for summarization of the video content, therefore, a state-of-the-art object detection algorithm, i.e., You Only Look Once (YOLOv5), has been employed. YOLOv5 is trained using a customised synthetic dataset of 600 annotated accident and non-accident video frames. Privacy preservation is achieved in two steps, firstly, a synthetic dataset is used for training and validation purposes, while, testing is performed on real-time data with an accuracy from 55% to 85%. Secondly, the real-time summarized videos (reduced video duration to 42.97% on average) are extracted and stored in an encrypted format to avoid un-trusted access to sensitive event-based data. Fernet, a symmetric encryption algorithm is applied to the summarized videos along with Diffie–Hellman (DH) key exchange algorithm and SHA256 hash algorithm. The encryption key is deleted immediately after the encryption process, and the decryption key is generated at the system of authorised stakeholders, which prevents the key from a man-in-the-middle (MITM) attack.

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Section: Video Summarization Through Yolomentioning

confidence: 99%

Privacy Preserved Video Summarization of Road Traffic Events for IoT Smart Cities

et al. 2023

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“…The model's average square error calculation produced the lowest error of 1.107. The imbrute retrieval of significant events and successive overview of sports videos using scoreboard monitoring are proposed in this study by Chakradhar Guntuboina et al ( 58 ) as a low-cost computational method. YOLO (You Only Look Once) a supervised—learning-based object detection algorithm.)…”

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 video summarization process gives a compact version of the original video for ease in managing the interesting video contents. Moreover, the video summarization methods capture the interest of the viewer by capturing exciting events from the original video [142,143]. Table 4 summarizes various proposed methodologies with their limitations to resolve various application issues in cricket.…”

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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Video Summarization for Multiple Sports Using Deep Learning

Cited by 21 publications

References 15 publications

Privacy Preserved Video Summarization of Road Traffic Events for IoT Smart Cities

Privacy Preserved Video Summarization of Road Traffic Events for IoT Smart Cities

Information communication and technology in sports: a meticulous review

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

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