Collective Sports: A multi-task dataset for collective activity recognition

Zalluhoglu, Cemil; Ikizler-Cinbis, Nazli

doi:10.1016/j.imavis.2020.103870

Cited by 26 publications

(12 citation statements)

References 11 publications

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“…The most commonly used volleyball dataset was proposed in 2016 and is limited to the domain of volleyball activity. Most algorithms achieve high accuracy in this dataset in which the best accuracy currently is 94.4% [21] . It will be worth studying whether the improvement obtained from current methods can scale up or are just the results of parameter regularization.…”

Section: Challenges and Trendsmentioning

confidence: 92%

“…Background clutter and occlusions between multiple people occur frequently. [12] BEHAVE 10 N/A 2009 Surveillance video 77.6%Zhang et al [13] CAD1 5 6 2009 Surveillance video 95.7% Tang et al [14] CAD2 6 8 2011 Surveillance video 85.5% Khamis et al [15] CAD3 6 3 2012 Surveillance video 87.2% Amer et al [16] UCLA Courtyard 6 10 2012 Surveillance video 83.7% Amer et al [17] Nursing Home 2 6 2012 Surveillance video 85.5% Deng et al [18] Broadcast Field Hockey 3 11 2012 Sports video 62.9% Lan et al [19] NCAA Basketball 11 N/A 2016 Sports video 58.1% Wu et al [20] Volleyball 8 8 2016 Sports video 94.4% Gavrilyuk et al [21] C-Sports 5 N/A 2020 Sports video 81.3% Zalluhoglu and Ikizler-Cinbis [22] NBA 9 N/A 2020 Sports video 47.5% Yan et al [23] (a)…”

Section: Surveillance Datasetsmentioning

confidence: 99%

“…Inspired by a transformer network [80] which relies on self-attention mechanisms to allow the network to adaptively extract the most relevant information and relationships, Gavrilyuk et al [21] proposed an actor-transformers network which learns interactions between the actors and adaptively extracts the important information for activity recognition.…”

Section: Deep Relationship Modelingmentioning

confidence: 99%

“…Deep relationship modeling stagNet (Qi et al [61] -18) ARG (Wu et al [64] -19) CRM (Azar et al [63] -19) Actor-transformer (Gavrilyuk et al [21] -20)…”

Section: Challenges and Trendsmentioning

confidence: 99%

See 3 more Smart Citations

A Comprehensive Review of Group Activity Recognition in Videos

Wang

Jian

et al. 2021

Int. J. Autom. Comput.

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Human group activity recognition (GAR) has attracted significant attention from computer vision researchers due to its wide practical applications in security surveillance, social role understanding and sports video analysis. In this paper, we give a comprehensive overview of the advances in group activity recognition in videos during the past 20 years. First, we provide a summary and comparison of 11 GAR video datasets in this field. Second, we survey the group activity recognition methods, including those based on handcrafted features and those based on deep learning networks. For better understanding of the pros and cons of these methods, we compare various models from the past to the present. Finally, we outline several challenging issues and possible directions for future research. From this comprehensive literature review, readers can obtain an overview of progress in group activity recognition for future studies.

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Section: Challenges and Trendsmentioning

confidence: 92%

Section: Surveillance Datasetsmentioning

confidence: 99%

Section: Deep Relationship Modelingmentioning

confidence: 99%

“…Deep relationship modeling stagNet (Qi et al [61] -18) ARG (Wu et al [64] -19) CRM (Azar et al [63] -19) Actor-transformer (Gavrilyuk et al [21] -20)…”

Section: Challenges and Trendsmentioning

confidence: 99%

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A Comprehensive Review of Group Activity Recognition in Videos

Wang

Jian

et al. 2021

Int. J. Autom. Comput.

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“…In the literature, most of HAR studies [12] focus on detecting daily living human activities such as walking, standing, downstairs, and upstairs. However, nowadays some HAR studies try to identify different types of activities such as transportation-based activities [13] (i.e., riding a car, bike, bus, or train), military-based activities [3] (i.e., run, jump, or jump-rope), and sports activities [14] (i.e., playing basketball, football, or volleyball). In addition to these high-level activities, some HAR studies have been focused on the transitions between the activities such as sit-to-stand or stand-to-sit [2].…”

Section: Related Workmentioning

confidence: 99%

Ordered physical human activity recognition based on ordinal classification

Daş¹,

Birant²

2021

Turk J Elec Eng & Comp Sci

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Human activity recognition (HAR) is a critical process for applications that focus on the classification of human physical activities such as jogging, walking, downstairs, and upstairs. Ordinal classification (OC) is a special type of supervised multi-class classification in which an inherent ordering among the classes exists, such as low, medium, and high. This study combines these two concepts and introduces an approach to "human activity recognition based on ordinal classification" (HAROC). In the proposed approach, ordinal classification is applied to human activity recognition where the physical activities can be ordered by using their signals' band power values. This is the first study that investigates the performance of the HAROC approach by combining the ordinal classification with eight different base learners.Besides, this study is also original in that it examines the effects of the demographic characteristics of the participants (i.e., gender, age, weight, and height) on the classification performance. The experiments carried out on a real-world dataset show that the proposed HAROC approach is an effective method for human activity recognition tasks.

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Sports analytics review: Artificial intelligence applications, emerging technologies, and algorithmic perspective

Ghosh

Ramamurthy

Chakma

et al. 2023

WIREs Data Min & Knowl

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The rapid and impromptu interest in the coupling of machine learning (ML) algorithms with wearable and contactless sensors aimed at tackling real‐world problems warrants a pedagogical study to understand all the aspects of this research direction. Considering this aspect, this survey aims to review the state‐of‐the‐art literature on ML algorithms, methodologies, and hypotheses adopted to solve the research problems and challenges in the domain of sports. First, we categorize this study into three main research fields: sensors, computer vision, and wireless and mobile‐based applications. Then, for each of these fields, we thoroughly analyze the systems that are deployable for real‐time sports analytics. Next, we meticulously discuss the learning algorithms (e.g., statistical learning, deep learning, reinforcement learning) that power those deployable systems while also comparing and contrasting the benefits of those learning methodologies. Finally, we highlight the possible future open‐research opportunities and emerging technologies that could contribute to the domain of sports analytics.This article is categorized under: Technologies > Machine Learning Technologies > Artificial Intelligence Technologies > Internet of Things

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Collective Sports: A multi-task dataset for collective activity recognition

Cited by 26 publications

References 11 publications

A Comprehensive Review of Group Activity Recognition in Videos

A Comprehensive Review of Group Activity Recognition in Videos

Ordered physical human activity recognition based on ordinal classification

Sports analytics review: Artificial intelligence applications, emerging technologies, and algorithmic perspective

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