A New Method for Ball Tracking Based on α-β, Linear Kalman and Extended Kalman Filters Via Bubble Sort Algorithm
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) arr...
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2018-06-01.
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| LEADER | 02340 am a22003013u 4500 | ||
|---|---|---|---|
| 001 | ijeecs12011_8461 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Nenavath, Hathiram |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Jatoth, Ravi Kumar |e author |
| 245 | 0 | 0 | |a A New Method for Ball Tracking Based on α-β, Linear Kalman and Extended Kalman Filters Via Bubble Sort Algorithm |
| 260 | |b Institute of Advanced Engineering and Science, |c 2018-06-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/12011 | ||
| 520 | |a 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. | ||
| 540 | |a Copyright (c) 2018 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |a Object tracking error; Bubble Sort; Algorithm; Background; Subtraction; α-β filter; LKF; EKF | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 10, No 3: June 2018; 989-999 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v10.i3 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/12011/8461 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/12011/8461 |z Get fulltext |