Automatic Broadcast Soccer Video Analysis, Player Detection, and Tracking Based on Color Histogram

Duh, Der-Jyh; Chen, Shuyuan; Kan, Cheng-Chung

doi:10.1007/978-1-4614-6747-2_15

Cited by 7 publications

(2 citation statements)

References 6 publications

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“…Compared with the wearable sensor-based methods, video-based automatic analysis methods are almost non-invasive. They have been widely used with the development of computer vision technology, such as football videos [ 26 , 27 ], basketball videos [ 28 , 29 ], tennis videos [ 30 , 31 ], and taekwondo videos [ 32 , 33 ]. However, due to the uncertainties and noisy backgrounds of ASD children’s action, there are few attempts to analyze ASD children’s videos.…”

Section: Introductionmentioning

confidence: 99%

Application of Skeleton Data and Long Short-Term Memory in Action Recognition of Children with Autism Spectrum Disorder

Zhang

Tian

et al. 2021

Sensors

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The recognition of stereotyped action is one of the core diagnostic criteria of Autism Spectrum Disorder (ASD). However, it mainly relies on parent interviews and clinical observations, which lead to a long diagnosis cycle and prevents the ASD children from timely treatment. To speed up the recognition process of stereotyped actions, a method based on skeleton data and Long Short-Term Memory (LSTM) is proposed in this paper. In the first stage of our method, the OpenPose algorithm is used to obtain the initial skeleton data from the video of ASD children. Furthermore, four denoising methods are proposed to eliminate the noise of the initial skeleton data. In the second stage, we track multiple ASD children in the same scene by matching distance between current skeletons and previous skeletons. In the last stage, the neural network based on LSTM is proposed to classify the ASD children’s actions. The performed experiments show that our proposed method is effective for ASD children’s action recognition. Compared to the previous traditional schemes, our scheme has higher accuracy and is almost non-invasive for ASD children.

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Section: Introductionmentioning

confidence: 99%

Application of Skeleton Data and Long Short-Term Memory in Action Recognition of Children with Autism Spectrum Disorder

Zhang

Tian

et al. 2021

Sensors

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“…We can also find in the literature examples that explore some aspects of the project, like video technology for coaching [4] and computerized video analysis of soccer [5]. Even so, none of those integrate all the technologies and goals of the Footdata project.…”

Section: Introductionmentioning

confidence: 99%

A Computer Vision Based Web Application for Tracking Soccer Players

Rodrigues

Cardoso

Vilas

et al. 2014

Universal Access in Human-Computer Interaction. Design and Development Methods for Universal Access

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Abstract. Soccer is a sport where everyone that is involved with it make all the efforts aiming for excellence. Not only the players need to show their skills on the pitch but also the coach, and the remaining staff, need to have their own tools so that they can perform at higher levels. Footdata is a project to build a new web application product for soccer (football), which integrates two fundamental components of this sport's world: the social and the professional. While the former is an enhanced social platform for soccer professionals and fans, the later can be considered as a Soccer Resource Planning, featuring a system for acquisition and processing information to meet all the soccer management needs. In this paper we focus only in a specific module of the professional component. We will describe the section of the web application that allows to analyse movements and tactics of the players using images directly taken from the pitch or from videos, we will show that it is possible to draw players and ball movements in a web application and detect if those movements occur during a game.

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A survey on soccer player detection and tracking with videos

Yang,

et al. 2024

Vis Comput

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Automatic Broadcast Soccer Video Analysis, Player Detection, and Tracking Based on Color Histogram

Cited by 7 publications

References 6 publications

Application of Skeleton Data and Long Short-Term Memory in Action Recognition of Children with Autism Spectrum Disorder

Application of Skeleton Data and Long Short-Term Memory in Action Recognition of Children with Autism Spectrum Disorder

A Computer Vision Based Web Application for Tracking Soccer Players

A survey on soccer player detection and tracking with videos

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