A New Method for Ball Tracking Based on α-β, Linear Kalman and Extended Kalman Filters Via Bubble Sort Algorithm

Hathiram Nenavath, Ravi Kumar Jatoth


Object tracking is one of the challenging issues in computer vision and video processing, which has several potential applications. In this paper, initially, a moving object is selected by frame differencing method and extracted the object by segment thresholding. The bubble sort algorithm (BSA) arranges the regions (large to small) to make sure that there is at least one big region (object) in object detection process. To track the object, a motion model is constructed to set the system models of Alpha-Beta (α-β) filter, Linear Kalman filter (LKF) and Extended Kalman filter (EKF). Many experiments have been conducted on balls with different sizes in image sequences and compared their tracking performance in normal light and bad light conditions. The parameters obtained are the root mean square error (RMSE), absolute error (AE), object tracking error (OTE), Tracking detection rate (TDR), and peak signal-to-noise ratio (PSNR) and they are compared to find the algorithm that performs the best for two conditions.


Object tracking error; Bubble Sort; Algorithm; Background; Subtraction; α-β filter; LKF; EKF

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DOI: http://doi.org/10.11591/ijeecs.v10.i3.pp989-999


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