Open AccessSoccer Players Detection Using GDLS Optimization and Spatial Bitwise Operation Filter
Author(s) -
Adhi Dharma Wibawa,
Atyanta Nika Rumaksari
Publication year - 2019
Publication title -
journal of data science and its applications (online)
Language(s) - English
Resource type - Journals
ISSN - 2614-7408
DOI - 10.21108/jdsa.2019.2.18
Subject(s) - background subtraction , computer vision , computer science , artificial intelligence , shadow (psychology) , pixel , bitwise operation , object (grammar) , noise (video) , filter (signal processing) , object detection , subtraction , image (mathematics) , pattern recognition (psychology) , mathematics , arithmetic , psychology , psychotherapist , programming language
Advancement computer vision technology in order to help coach creates strategy has been affecting the sport industry evolving very fast. Players movement patterns and other important behavioral activities regarding the tactics during playing the game are the most important data obtained in applying computer vision in Sport Industry. The basic technique for extracting those information during the game is player detection. Three fundamental challenges of computer vision in detecting objects are random object’s movement, noise and shadow. Background subtraction is an object’s detection method that used widely for separating moving object as foreground and non moving object as background. This paper proposed a method for removing shadow and unwanted noise by improving traditional background subtraction technique. First, we employed GDLS algorithm to optimize background-foreground separation. Then, we did filter shadows and crumbs-like object pixels by applying digital spatial filter which is created from implementation of digital arithmetic algorithm (bitwise operation). Finally, our experimental result demonstrated that our algorithm outperform conventional background subtraction algorithms. The experiments result proposed method has obtained 80.5% of F1-score with average 20 objects were detected out of 24 objects.