2022
DOI: 10.1117/1.jei.32.1.011003
|View full text |Cite
|
Sign up to set email alerts
|

YOLOv3-SORT: detection and tracking player/ball in soccer sport

Abstract: .Soccer player and ball detection and tracking have emerged as an area of intense interest among many analysts and researchers. This is because it aids coaches in team performance evaluation and decision-making to achieve optimal results. However, existing methodologies have failed to effectively detect and track the ball when it moves at high velocity and also to track players under occlusion conditions. You only look once (YOLOv3) and simple online real-time (SORT)-based soccer ball and player tracking appro… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
9
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
5
3
1

Relationship

0
9

Authors

Journals

citations
Cited by 15 publications
(9 citation statements)
references
References 32 publications
0
9
0
Order By: Relevance
“…Another process of player position and motion acquisition (tracking) in the court is made by video match analysis [207,208]. This computer-aided system takes place in individual and team sports analysis using video recordings of the match.…”
Section: Position Measurement On the Playing Court By Video Analysismentioning
confidence: 99%
“…Another process of player position and motion acquisition (tracking) in the court is made by video match analysis [207,208]. This computer-aided system takes place in individual and team sports analysis using video recordings of the match.…”
Section: Position Measurement On the Playing Court By Video Analysismentioning
confidence: 99%
“…1 ), so the width and height size output of the object can still be trained through L1 loss, shown as Equation (16).…”
Section: Detection Modulementioning
confidence: 99%
“…On the other hand, most recent sports video detection and tracking techniques directly adapt general detection and tracking methods to sports-specific applications. For instance, Naik et al [16], Vicent et al [17], and Huang [15] all employ the YOLO series of methods, while Kevca et al [18] employ several classic general lightweight detection models. While this straightforward adaptation may be convenient, it often overlooks the unique challenges encountered in sports scenarios, such as motion blur, occlusion, and other issues.…”
Section: Introductionmentioning
confidence: 99%
“…In such a case, a jersey number must be detected to recognize the player [60]. Accurate tracking [61][62][63][64][65][66][67][68][69][70][71][72] by detection [73][74][75][76] of multiple soccer players as well as the ball in real-time is a major challenge to evaluate the performance of the players, to find their relative positions at regular intervals, and to link spatiotemporal data to extract trajectories. The systems which evaluate the player [77] or team performance [78] have the potential to understand the game's aspects, which are not obvious to the human eye.…”
Section: Soccermentioning
confidence: 99%