Performing the Kick During Walking for RoboCup 3D Soccer Simulation League Using Reinforcement Learning Algorithm

Rezaeipanah, Amin; Amiri, Parvin; Jafari, Shahram

doi:10.1007/s12369-020-00712-2

Cited by 16 publications

(6 citation statements)

References 54 publications

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“…Indeed, there are new areas of research in which the importance of human-automatic tracking in video is increasing, such as human-computer interaction and virtual reality. [22][23][24] In general, object detection, motion, action, activity, and behavior in the field of computer vision with the applications of monitoring, retrieval and human-computer interaction is of great importance. 25 In recent years, a large dataset has been produced to challenge researchers in these fields.…”

Section: Gigabytementioning

confidence: 99%

“…In particular, automatic human tracking in video has always been an interesting topic for researchers, as it is a multidisciplinary field of research with multiple applications. Indeed, there are new areas of research in which the importance of human‐automatic tracking in video is increasing, such as human‐computer interaction and virtual reality 22‐24 …”

Section: Introductionmentioning

confidence: 99%

“…Human behavior detection is used in many applications such as video surveillance, motion detection, and human‐computer interaction 17 . Extensive applications of human behavior detection have led to the development of various systems in areas such as care, control, and analysis 24 . Therefore, it is important to know the HBA and the related datasets.…”

Section: Introductionmentioning

confidence: 99%

“…17 Extensive applications of human behavior detection have led to the development of various systems in areas such as care, control, and analysis. 24 Therefore, it is important to know the HBA and the related datasets. In this section, we provide the motivation and then explain the concept of data mining and big data in IoT.…”

Section: Introductionmentioning

confidence: 99%

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A systematic survey of data mining and big data in human behavior analysis: Current datasets and models

Ding

Gan

Bahrami

2022

Trans Emerging Tel Tech

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In recent years, understanding human behavior has become one of the most important topics in the field of computer vision research. The reason for this growing attention is the wide range of applications that can benefit from the results of this field of research. The human behavior analysis (HBA) includes a wide range of research areas from the detection of human motion and action.Datasets created through actions and detection of human activities make it possible to compare different detection methods with the same input data. Data mining and big data approaches are very popular for analyzing data related to human behavior and can be used to address the challenges of fast processing. This article provides a systematic survey of data mining and big data in the HBA. We focus on current datasets and models related to the detection of human behavior patterns in the literature. The purpose of this survey is to assist researchers in select appropriate datasets and models to evaluate algorithms, as well as to identify research gaps for future work. To achieve this goal, articles published between 2010 and 2021 have been reviewed. These articles fall into five general categories in terms of dataset focus: object detection, motion, action, activity, and behavior. This article provides a summary of data mining and big data models in the HBA, as well as related datasets based on these categories, to highlight promising research avenues for future work.

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Section: Gigabytementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A systematic survey of data mining and big data in human behavior analysis: Current datasets and models

Ding

Gan

Bahrami

2022

Trans Emerging Tel Tech

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“…These studies collectively represent a multidisciplinary effort to unravel the intricacies of tactical optimization in sports, offering valuable insights for coaches, analysts, and researchers alike Rezaeipanah et al (2021). tackle the challenge of performing kicks during walking for robot soccer players in the RoboCup 3D Soccer Simulation League, employing reinforcement learning algorithms to optimize robotic actions.…”

mentioning

confidence: 99%

Tactical Optimization of Soccer Game Based on Motion Trajectory Analysis Algorithm

Wang

2024

jes

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Tactical optimization of soccer games using motion trajectory analysis algorithms involves the utilization of advanced computational techniques to analyze player movements and optimize team strategies. By applying motion trajectory analysis algorithms to tracking data obtained from sensors or video feeds, coaches and analysts can gain insights into player positioning, ball movement patterns, and team interactions during matches. This analysis helps identify tactical strengths and weaknesses, assess opponent strategies, and inform adjustments to game plans in real-time or during post-match analysis. By leveraging these algorithms, teams can refine their tactics, improve performance, and gain a competitive edge on the field. This paper introduces the Motion Trajectory Trigonometric Multi-Objective (MTTM-O) framework as a comprehensive approach to optimizing tactical decisions and predicting match outcomes in soccer. With trigonometric functions to model player trajectories and multi-objective optimization techniques, MTTM-O offers insights into the impact of tactical formations, expected goals, ball possession, and defensive solidity on match results. Through scenario analysis and multi-objective optimization, we demonstrate the effectiveness of MTTM-O in enhancing strategic decision-making and performance analysis in soccer. Findings underscore the importance of tactical flexibility, adaptation to opponent strategies, and the balance between offensive and defensive aspects of gameplay for success on the field. Through multi-objective optimization, MTTM-O evaluates various tactical formations and their impact on match results. For instance, in scenario analysis, employing a 3-5-2 formation resulted in an expected goal (xG) of 2.2 and a ball possession percentage of 62%, leading to a decisive win. Conversely, a 4-4-2 formation yielded lower xG (0.8) and ball possession (47%), resulting in a draw. These findings highlight the importance of tactical flexibility and adaptation to opponent strategies for success on the field.

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Towards improving context-aware applications using link quality induction and self-calibrated ultra-wide band localization systems

Lai,

Zhou,

et al. 2024

Computing

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Performing the Kick During Walking for RoboCup 3D Soccer Simulation League Using Reinforcement Learning Algorithm

Cited by 16 publications

References 54 publications

A systematic survey of data mining and big data in human behavior analysis: Current datasets and models

A systematic survey of data mining and big data in human behavior analysis: Current datasets and models

Tactical Optimization of Soccer Game Based on Motion Trajectory Analysis Algorithm

Towards improving context-aware applications using link quality induction and self-calibrated ultra-wide band localization systems

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