A video-based framework for automatic 3d localization of multiple basketball players : a combinatorial optimization approach

Sports complexity must be investigated at competitions; therefore, non-invasive methods are essential. In this context, computer vision, image processing, and machine learning techniques can be useful in designing a non-invasive system for data acquisition that identifies players’ positions in official basketball matches. Here, we propose and evaluate a novel video-based framework to perform automatic 3D localization of multiple basketball players. The introduced framework comprises two parts. The first stage is player detection, which aims to identify players’ heads at the camera image level. This stage is based on background segmentation and on classification performed by an artificial neural network. The second stage is related to 3D reconstruction of the player positions from the images provided by the different cameras used in the acquisition. This task is tackled by formulating a constrained combinatorial optimization problem that minimizes the re-projection error while maximizing the number of detections in the formulated 3D localization problem8286CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPNão temNão temNão temWe would like to thank the CAPES, FAEPEX, FAPESP, and CNPq for funding their research. This paper has content from master degree’s dissertation previously published (Monezi, 2016) and available onlin

KINEMATICAL ANALYSIS OF BANDAL AND DOLYO TAEKWONDO KICKS OF A HIGH LEVEL FEMALE ATHLETE

The purpose of this study was to analyze the kinematics of two Taekwondo kicks performed by a high level female athlete. The kicks were compared according to three main factors: a) type of kick (Bandal or Dolyo), b) foot of kick (right or left foot); c) kick starting with the kicking foot in front of the support limb or behind (front or back foot).The DVideo kinematical analysis system was used to reconstruct the 3D coordinates of twelve landmarks. The study was able to identify the main differences between the two most frequently used Taekwondo kicks, showed that the initial condition should be considered when comparing kicks and revealed differences between kicks accomplished with right or left foot of this high level athlete

A 3D KINEMATICAL ANALYSIS OF LONG JUMP IN THE “GOLD MEETING RIO OF ATHLETICS 2007”

This study was based on the 3D kinematical analysis of long jump in an official competition of the International Association of Athletics Federation. A six camera kinematical analysis system was used to reconstruct the 3D coordinates of eighteen points, modeling the athlete’s body with the follow segments: head, trunk, arms, forearms, thighs, calves and feet. Several performance variables concerning the center of mass trajectories and velocities were used to characterize and compare the individual jumps. Descriptive statistics was used to compare the results obtained with those found in the literature

COVERED DISTANCES OF HANDBALL PLAYERS OBTAINED BY AN AUTOMATIC TRACKING METHOD

The aim of this work is to obtain the distances covered by handball players and their velocities during a match using a new approach based on automatic tracking method described in Figueroa et. al. (2006a, 2006b) and the Adaboost detector (Okuma, 2004). A whole game of a Brazilian regional handball championship for players under age of 21 was recorded. Applying the mentioned automatic tracking, the accumulated covered distances and the velocities were calculated for all the players. The results of average covered distances (±SD) in the 1st and 2 nd halves were 2199(±230) and 2453(±214). The results of covered distances and the velocities allow individual and collective analyses of the players by the team staff. The proposed method revealed to be a powerful tool to improve physical analysis of the handball players

Analysis of the distances covered by first division Brazilian soccer players obtained with an automatic tracking method

Methods based on visual estimation still is the most widely used analysis of the distances that is covered by soccer players during matches, and most description available in the literature were obtained using such an approach. Recently, systems based on computer vision techniques have appeared and the very first results are available for comparisons. The aim of the present study was to analyse the distances covered by Brazilian soccer players and compare the results to the European players', both data measured by automatic tracking system. Four regular Brazilian First Division Championship matches between different teams were filmed. Applying a previously developed automatic tracking system (DVideo, Campinas, Brazil), the results of 55 outline players participated in the whole game (n = 55) are presented. The results of mean distances covered, standard deviations (s) and coefficient of variation (cv) after 90 minutes were 10,012 m, s = 1,024 m and cv = 10.2%, respectively. The results of three-way ANOVA according to playing positions, showed that the distances covered by external defender (10642 ± 663 m), central midfielders (10476 ± 702 m) and external midfielders (10598 ± 890 m) were greater than forwards (9612 ± 772 m) and forwards covered greater distances than central defenders (9029 ± 860 m). The greater distances were covered in standing, walking, or jogging, 5537 ± 263 m, followed by moderate-speed running, 1731 ± 399 m; low speed running, 1615 ± 351 m; high-speed running, 691 ± 190 m and sprinting, 437 ± 171 m. Mean distance covered in the first half was 5,173 m (s = 394 m, cv = 7.6%) highly significant greater (p < 0.001) than the mean value 4,808 m (s = 375 m, cv = 7.8%) in the second half. A minute-by-minute analysis revealed that after eight minutes of the second half, player performance has already decreased and this reduction is maintained throughout the second half. ©Journal of Sports Science and Medicine (2007)

Measuring Handball Players Trajectories Using An Automatically Trained Boosting Algorithm.

The aim of the present paper is to propose and evaluate an automatically trained cascaded boosting detector algorithm based on morphological segmentation for tracking handball players. The proposed method was able to detect correctly 84% of players when applied to the second period of that same game used for training and 74% when applied to a different game. Furthermore, the analysis of the automatic training using boosting detector revealed general results such as the training time initially increased with the number of figures used, but as more figures were added, the training time decreased. Automatic morphological segmentation has shown to be a fast and efficient method for selecting image regions for the boosting detector and allowed an improvement in the automatic tracking of handball players.1453-6