Abstract
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.
Highlights
Automatic multiple objects detection in soccer has played an important role in game monitoring system.Due to high demand of implementing information technology in metric measurement began enormously emerging, research on player’s automatic annotation system is flourishing astonishingly
Some previous works have the focus on developing automatic detection and tracking algorithm using background subtraction technique [2],[3],[4]
Background subtraction technique is the process based on field image processing technique wherein a foreground is separated from its background in order to further processing
Summary
Automatic multiple objects detection in soccer has played an important role in game monitoring system.Due to high demand of implementing information technology in metric measurement began enormously emerging, research on player’s automatic annotation system is flourishing astonishingly. It helps performance analysts and coaches to plan future strategies Nowadays, this technique has played a crucial role in premiere leagues due to its impact on the competition. Some previous works have the focus on developing automatic detection and tracking algorithm using background subtraction technique [2],[3],[4]. There is a challenge in traditional background subtracting method where more than half of the required training images are foreground. Background maintenance will maintain the change of background initialization, while background initialization will adapt to waking or moving background by taking an average of sampled frames. As a result this will produce an image that contains only the background without foreground
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