Synthesis of supervised classification algorithm using intelligent and statistical tools

TL;DR

This paper introduces a non-parametric, robust color image segmentation algorithm for foreground object detection in sports scenes, aiding tactical analysis and team identification in football matches.

Contribution

It presents a novel non-parametric segmentation method that improves object detection robustness and supports automated team identification in sports video analysis.

Findings

Effective segmentation even with shadows and highlights

Successful team identification in real football match

Potential for enhancing sports analytics and computer vision applications

Abstract

A fundamental task in detecting foreground objects in both static and dynamic scenes is to take the best choice of color system representation and the efficient technique for background modeling. We propose in this paper a non-parametric algorithm dedicated to segment and to detect objects in color images issued from a football sports meeting. Indeed segmentation by pixel concern many applications and revealed how the method is robust to detect objects, even in presence of strong shadows and highlights. In the other hand to refine their playing strategy such as in football, handball, volley ball, Rugby..., the coach need to have a maximum of technical-tactics information about the on-going of the game and the players. We propose in this paper a range of algorithms allowing the resolution of many problems appearing in the automated process of team identification, where each player is affected to his corresponding team relying on visual data. The developed system was tested on a match of the Tunisian national competition. This work is prominent for many next computer vision studies as it's detailed in this study.