port action mining: Dribbling recognition in socce

Author(s)

Barbon Junior, Sylvio ^[1] ; Pinto, Allan ^{[2, 3]} ; Barroso, Joao Vitor ^[1] ; Caetano, Fabio Giuliano ^[4] ; Moura, Felipe Arruda ^[4] ; Cunha, Sergio Augusto ^[3] ; Torres, Ricardo da Silva ^[5]

Affiliation

^[1] Londrina State Univ UEL, Dept Comp Sci, Londrina, Parana - Brazil

^[2] Univ Campinas UNICAMP, Inst Comp, Campinas, SP - Brazil

^[3] Univ Campinas UNICAMP, Sch Phys Educ, Campinas, SP - Brazil

^[4] State Univ Londrina UEL, Sport Sci Dept, Londrina, Parana - Brazil

^[5] NTNU Norwegian Univ Sci & Technol, Dept ICT & Naural Sci, Alesund - Norway

• Document type: Journal article
• Source: MULTIMEDIA TOOLS AND APPLICATIONS; v. 81, n. 3 DEC 2021.
• Web of Science Citations: 0

Abstract

Recent advances in Computer Vision and Machine Learning empowered the use of image and positional data in several high-level analyses in Sports Science, such as player action classification, recognition of complex human movements, and tactical analysis of team sports. In the context of sports action analysis, the use of positional data allows new developments and opportunities by taking into account players' positions over time. Exploiting the positional data and its sequence in a systematic way, we proposed a framework that bridges association rule mining and action recognition. The proposed Sports Action Mining (SAM) framework is grounded on the usage of positional data for recognising actions, e.g., dribbling. We hypothesise that different sports actions could be modelled using a sequence of confidence levels computed from previous players' locations. The proposed method takes advantage of an association rule mining algorithm (e.g., FPGrowth) to generate displacement sequences for modelling actions in soccer. In this context, transactions are sequences of traces representing player displacements, while itemsets are players' coordinates on the pitch. The experimental results pointed out the Random Forest classifier achieved a balanced accuracy value of 93.3% for detecting dribbling actions, which are considered complex events in soccer. Additionally, the proposed framework provides insights on players' skills and player's roles based on a small amount of positional data. (AU)

• FAPESP's process: 18/19007-9 - Find dribbling time series in a physical-tactical-technical scenario during official professional matches
• Grantee: Sergio Augusto Cunha
• Support Opportunities: Scholarships abroad - Research
• FAPESP's process: 19/17729-0 - Data-driven approaches for soccer match analysis: an e-Science perspective
• Grantee: Paulo Roberto Pereira Santiago
• Support Opportunities: Regular Research Grants
• FAPESP's process: 19/16253-1 - Unraveling the secret of Brazilian and Dutch soccer by capturing successful elements of playing style and playing strategies
• Grantee: Allan da Silva Pinto
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 16/50250-1 - The secret of playing football: Brazil versus the Netherlands
• Grantee: Sergio Augusto Cunha
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 17/20945-0 - Multi-user equipment approved in great 16/50250-1: local positioning system
• Grantee: Sergio Augusto Cunha
• Support Opportunities: Multi-user Equipment Program
• FAPESP's process: 19/22262-3 - Large volume reconstruction: high precision system for position detection in sports
• Grantee: Paulo Roberto Pereira Santiago
• Support Opportunities: Organization Grants - Scientific Meeting