Ball Tracking System Using Kalman Filter and Deep Learning
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Abstract
This paper presents a method for detecting the positions of tennis balls in video files using deep learning for ball position detection in video images. However, there are some image frames where deep learning cannot accurately detect the ball positions due to the small size of the tennis balls and their fast movement in that particular frame. To address this issue, when the deep learning system fails to track the movement of the tennis balls, a Kalman filter is used to estimate the positions of the balls in those frames and replace the missing positions with the estimated values. Experimental results demonstrate that the proposed method can closely approximate the actual positions of the tennis balls in the image frames with high accuracy.
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