Multi-camera multi-player tracking with deep player identification in sports video

Zhang, Ruiheng; Wu, Lingxiang; Yang, Yukun; Wu, Wanneng; Chen, Yueqiang; Xu, Min

doi:10.1016/j.patcog.2020.107260

Cited by 76 publications

(30 citation statements)

References 18 publications

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“…SG systems in sports applications are diverse, performing data engineering ( Johnson et al, 2021 ) and data labeling ( Cronin et al, 2019 ), evaluating the role of the shoe structure on running biomechanics ( Onodera et al, 2017 ), monitoring fatigue to prevent injury ( Russell et al, 2021 ), ( Zhang J. et al, 2014 ), counting steps ( Kang et al, 2018 ), assessing physical autonomy and functional ability ( Khan et al, 2015 ), articulating real-time control of an electrical muscle stimulation (EMS) device for sports training ( Hassan et al, 2017 ), predicting and preventing injury ( Taborri et al, 2021 ), achieving multi-player tracking, identification, and re-identification ( Zhang R. et al, 2020 ), classifying and counting different sports activities ( Sundholm et al, 2014 ), and recognizing and analyzing sports behavior ( Guo and Wang, 2021 ).…”

Section: Health and Wellnessmentioning

confidence: 99%

“…The individuality of the gait pattern persists over time ( Horst et al, 2017 ) and many pathologies. The main applications of gait recognition are person identification, person re-identification, person authentication, gender recognition, age estimation ( Dindorf et al, 2020a ), occupancy sensing ( Yang et al, 2018 ), crowd density estimation ( Zhou et al, 2018 ), crowd monitoring and anomaly detection for video surveillance applications ( Sun et al, 2017 ), and multi-player tracking and identification ( Zhang R. et al, 2020 ). We will look into the first four categories in more detail in sections 6.1 through 6.4.…”

Section: Gait Based Biometricsmentioning

confidence: 99%

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A Survey of Human Gait-Based Artificial Intelligence Applications

2022

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We performed an electronic database search of published works from 2012 to mid-2021 that focus on human gait studies and apply machine learning techniques. We identified six key applications of machine learning using gait data: 1) Gait analysis where analyzing techniques and certain biomechanical analysis factors are improved by utilizing artificial intelligence algorithms, 2) Health and Wellness, with applications in gait monitoring for abnormal gait detection, recognition of human activities, fall detection and sports performance, 3) Human Pose Tracking using one-person or multi-person tracking and localization systems such as OpenPose, Simultaneous Localization and Mapping (SLAM), etc., 4) Gait-based biometrics with applications in person identification, authentication, and re-identification as well as gender and age recognition 5) “Smart gait” applications ranging from smart socks, shoes, and other wearables to smart homes and smart retail stores that incorporate continuous monitoring and control systems and 6) Animation that reconstructs human motion utilizing gait data, simulation and machine learning techniques. Our goal is to provide a single broad-based survey of the applications of machine learning technology in gait analysis and identify future areas of potential study and growth. We discuss the machine learning techniques that have been used with a focus on the tasks they perform, the problems they attempt to solve, and the trade-offs they navigate.

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Section: Health and Wellnessmentioning

confidence: 99%

Section: Gait Based Biometricsmentioning

confidence: 99%

A Survey of Human Gait-Based Artificial Intelligence Applications

2022

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“…In Reference [44], a robust multi-camera multi-player tracking framework is presented. In this system, the player identity, which is commonly ignored in existing methods, is specifically considered, using a deep player identification model for players' identification, 2D localization and segmentation based on a Cascade Mask R-CNN model.…”

Section: Related Workmentioning

confidence: 99%

Recurrent Neural Network for Human Activity Recognition in Embedded Systems Using PPG and Accelerometer Data

et al. 2021

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Photoplethysmography (PPG) is a common and practical technique to detect human activity and other physiological parameters and is commonly implemented in wearable devices. However, the PPG signal is often severely corrupted by motion artifacts. The aim of this paper is to address the human activity recognition (HAR) task directly on the device, implementing a recurrent neural network (RNN) in a low cost, low power microcontroller, ensuring the required performance in terms of accuracy and low complexity. To reach this goal, (i) we first develop an RNN, which integrates PPG and tri-axial accelerometer data, where these data can be used to compensate motion artifacts in PPG in order to accurately detect human activity; (ii) then, we port the RNN to an embedded device, Cloud-JAM L4, based on an STM32 microcontroller, optimizing it to maintain an accuracy of over 95% while requiring modest computational power and memory resources. The experimental results show that such a system can be effectively implemented on a constrained-resource system, allowing the design of a fully autonomous wearable embedded system for human activity recognition and logging.

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“…In this connection, some of the proposed solutions include an automatic target detector in infrared imagery [15], a multi-remote-sensor approach, such as a deeplearning-based burned area mapping using the synergy of the satellite data and images of Sentinels 1 and 2 [16]. Moreover, there is a new method that could be potentially adopted, i.e., the use of multi-camera multi-player tracking with deep player identification [17]; however, the cost-effectiveness in a distributed energy grid context should be assessed.…”

Section: Observability Of the Gridmentioning

confidence: 99%

Developing Novel Technologies and Services for Intelligent Low Voltage Electricity Grids: Cost–Benefit Analysis and Policy Implications

et al. 2021

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The paper presents a set of prototype smart grid technologies and services and validates the economic viability of the proposed solution using cost–benefit analysis (CBA). The study considered the EU-funded project called RESOLVD and implemented the technologies and services in a real-life pilot. the technologies and services on the EU-funded H2020. The paper focuses on the analysis of technological solutions which enhance the operational efficiency and the hosting capacity of low-voltage electricity distribution grids. The solutions provided better integration of a hybrid battery storage system, with the grid interfacing power electronics, smart gateways for the interconnection of assets at the grid edge, and sensors enhancing infrastructure observability and control. The result from the CBA indicates the economic viability of the project, high scalability, and replicability. The economic benefits were realized with the breakeven value of eight secondary substations (SS) and 16 feeders. The scenario test on the DSO’s willingness to pay for the software as a service (SaaS) revealed that the payback period can further be reduced by almost half with a higher internal rate of return (IRR) and net present value (NPV). Both the CBA and scenario tests showed RESOLVD solution can become more economically viable when deployed in largescale. Moreover, the CBA results provide evidence to the energy policy by allowing DSOs to consider both CAPEX and OPEX for better investment decisions. Further, the paper proposes an alternative business approach that shifts from grid reinforcement to service provision. The paper also discusses the research implications on energy policy and business.

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Multi-camera multi-player tracking with deep player identification in sports video

Cited by 76 publications

References 18 publications

A Survey of Human Gait-Based Artificial Intelligence Applications

A Survey of Human Gait-Based Artificial Intelligence Applications

Recurrent Neural Network for Human Activity Recognition in Embedded Systems Using PPG and Accelerometer Data

Developing Novel Technologies and Services for Intelligent Low Voltage Electricity Grids: Cost–Benefit Analysis and Policy Implications

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