Particle filter algorithm based spatial motion tracking of football landing location

Ye, Zhijing; Ye, Haixiong

doi:10.1007/s11042-018-6307-8

Cited by 7 publications

(4 citation statements)

References 8 publications

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“…Many efficient algorithms such as twin algorithms (Figure 1) have emerged and are widely used in target detection and tracking. e target tracking technology plays an essential role in football video analysis [21]. As the most watched sport in the world, football is a rich and widespread sport with huge commercial value.…”

Section: Introductionmentioning

confidence: 99%

Multitarget Tracking Algorithm in Intelligent Analysis of Football Movement Training Stance

Peng

2022

Security and Communication Networks

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In recent years, with the continuous development of computer technology, deep learning has been widely applied to computer vision tasks and has achieved great success in areas such as visual detection and tracking. On this basis, making deep learning techniques truly accessible to people becomes the next objective. Target detection and tracking in football gesture training is a quite challenging task with great practical and commercial value. In traditional football training methods, target trajectories are often extracted by means of a recording chip carried by the player. However, the cost of this method is high and it is difficult to replicate in amateur stadiums. Some studies have also used only cameras to process targets in football videos. However, due to the similarity in appearance and frequent occlusion of targets in football videos, these methods often only segment targets such as players and balls in the image but do not allow them to be tracked. Target tracking techniques are of great importance in football training and are the basis for tasks such as player training analysis and match strategy development. In recent years, many excellent algorithms have emerged in the field of target tracking, mainly in the categories of correlation filtering and deep learning, but none of them are able to achieve high accuracy in player tracking for football training videos. After all, the problem of locating clips of interest to athletes from a full-length video is a pressing one. Traditional machine learning-based approaches to sports event detection have poor accuracy and are limited in the types of events they can detect. These traditional methods often rely on auxiliary information such as audio commentary and relevant text, which are less stable than video. In recent years, deep learning-based methods have made great progress in the detection of single-player video events and actions, but less so in the detection of sports video events. As a result, there are few sports video datasets that can be used for deep learning training. Based on research in computer vision and deep learning, this paper designs a multitarget tracking system for football training. To be specific, this algorithm uses multiple cameras for image acquisition in the stadium in order to accurately track multiple targets in the stadium over time. Furthermore, the framework for a single camera multitarget tracking approach has been designed based on deep learning-based visual detection methods and correlation filter-based tracking methods. This framework focuses on using data correlation algorithms to fuse the results of detectors and trackers so that multiple targets can be tracked accurately in a single camera. To sum up, this research allows for robust and real-time long-term accurate tracking of targets in football training videos through multitarget tracking algorithms and the intercorrection of multiple camera systems.

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Section: Introductionmentioning

confidence: 99%

Multitarget Tracking Algorithm in Intelligent Analysis of Football Movement Training Stance

Peng

2022

Security and Communication Networks

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“…We construct a flight kinematics model for the trajectory tracking of the ball in football training based on the control as mentioned above constraints of the trajectory tracking of the ball drop point [6]. We construct a linear control matrix of dynamic motion primitives for tracking the ball's trajectory in football training under a single degree of freedom.…”

Section: Constraint Parameter Adjustment Of Trajectory Tracking Controlmentioning

confidence: 99%

Football Offense Training Strategy Based on Fractional Differential Mathematical Modeling

Liu

Chu

2022

Applied Mathematics and Nonlinear Sciences

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This paper establishes the parameters of the ball trajectory based on the fractional differential equation model. At the same time, this paper quantifies the shooting power of direct free kicks. In this paper, the resistance effect of air on the motion of the center of mass is considered. And the dynamic equation of the football arc motion is established. In this way, we obtain the analytical solution of the differential equation of order. The research shows that the correlation between the fitting equation of the football flight trajectory and the prediction equation is 96.55%. The algorithm model in this paper can effectively predict the flight path of football. The research theory of this paper provides a theoretical basis for football offensive training strategies.

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“…This study analyzed motion by applying particle filter algorithm to the motion data. 19 There is also a study using k-nearest neighbors algorithm to identify similarities between matches. The study identified most similar game to a particular game and applied them to betting based on the results of that game.…”

Section: Related Workmentioning

confidence: 99%

“…It used the motion tracking system APRBA to identify players' movements. This study analyzed motion by applying particle filter algorithm to the motion data 19 …”

Section: Related Workmentioning

confidence: 99%

Exploiting weighted association rule mining for indicating synergic formation tactics in soccer teams

Lee

Jung

Camacho

2021

Concurrency and Computation

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Managers make decisions on team tactics, formations, and player selection based on their own experiences. The managers have limitations in understanding the team's situation and sometimes they can think wrong. The purpose of this study is to make decisions on player selection and tactical formation according to the level of the opponent based on the data, not on the intuition of the manager. In our previous study, the Boruta algorithm was used to extract important features from 69 features in soccer player data by position. The detailed roles of each position were defined by using K-means algorithm. For example, the detailed roles of each position were defined as Mezzala, Shadow Striker, Deep-lying playmaker, and so on. That is, forward positions are classified as Target Man (TM) and Shadow Striker (SS). TM is a high-goal, high-competitive forward, and SS is a high-dribble, high-pass forward. In this study, we analyze a clustering dataset and the game appearance dataset. The game appearance dataset are divided into CL (Champions league Level), EL (Europa league Level), ML (Middle Level), and RL (Relegation Level). Association rule mining algorithm analyzes the synergy between positions, and selects a position with high synergy. Weighted association rule mining algorithm establishes player selection and tactical formation with the weight, which is the player's rating data. Finally, using the obtained results, we visualize the synergy between positions, tactical formation, and player characteristics depending on the level of the opponent.

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Particle filter algorithm based spatial motion tracking of football landing location

Cited by 7 publications

References 8 publications

Multitarget Tracking Algorithm in Intelligent Analysis of Football Movement Training Stance

Multitarget Tracking Algorithm in Intelligent Analysis of Football Movement Training Stance

Football Offense Training Strategy Based on Fractional Differential Mathematical Modeling

Exploiting weighted association rule mining for indicating synergic formation tactics in soccer teams

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