CLC number: TP391.41
On-line Access: 2017-04-12
Received: 2016-08-14
Revision Accepted: 2016-12-21
Crosschecked: 2017-03-28
Cited: 0
Clicked: 6439
Rong-Feng Zhang , Ting Deng , Gui-Hong Wang , Jing-Lun Shi , Quan-Sheng Guan . A robust object tracking framework based on a reliable point assignment algorithm[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(4): 545-558.
@article{title="A robust object tracking framework based on a reliable point assignment algorithm",
author="Rong-Feng Zhang , Ting Deng , Gui-Hong Wang , Jing-Lun Shi , Quan-Sheng Guan ",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="18",
number="4",
pages="545-558",
year="2017",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1601464"
}
%0 Journal Article
%T A robust object tracking framework based on a reliable point assignment algorithm
%A Rong-Feng Zhang
%A Ting Deng
%A Gui-Hong Wang
%A Jing-Lun Shi
%A Quan-Sheng Guan
%J Frontiers of Information Technology & Electronic Engineering
%V 18
%N 4
%P 545-558
%@ 2095-9184
%D 2017
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1601464
TY - JOUR
T1 - A robust object tracking framework based on a reliable point assignment algorithm
A1 - Rong-Feng Zhang
A1 - Ting Deng
A1 - Gui-Hong Wang
A1 - Jing-Lun Shi
A1 - Quan-Sheng Guan
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 18
IS - 4
SP - 545
EP - 558
%@ 2095-9184
Y1 - 2017
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1601464
Abstract: Visual tracking, which has been widely used in many vision fields, has been one of the most active research topics in computer vision in recent years. However, there are still challenges in visual tracking, such as illumination change, object occlusion, and appearance deformation. To overcome these difficulties, a reliable point assignment (RPA) algorithm based on wavelet transform is proposed. The reliable points are obtained by searching the location that holds local maximal wavelet coefficients. Since the local maximal wavelet coefficients indicate high variation in the image, the reliable points are robust against image noise, illumination change, and appearance deformation. Moreover, a kalman filter is applied to the detection step to speed up the detection processing and reduce false detection. Finally, the proposed RPA is integrated into the tracking-learning-detection (TLD) framework with the kalman filter, which not only improves the tracking precision, but also reduces the false detections. Experimental results showed that the new framework outperforms TLD and kernelized correlation filters with respect to precision, f-measure, and average overlap in percent.
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