TY - JOUR
T1 - Visual tracking using objectness-bounding box regression and correlation filters
AU - Mbelwa, Jimmy T.
AU - Zhao, Qingjie
AU - Lu, Yao
AU - Wang, Fasheng
AU - Mbise, Mercy
N1 - Publisher Copyright:
© 2018 SPIE and IS&T.
PY - 2018/3/1
Y1 - 2018/3/1
N2 - Visual tracking is a fundamental problem in computer vision with extensive application domains in surveillance and intelligent systems. Recently, correlation filter-based tracking methods have shown a great achievement in terms of robustness, accuracy, and speed. However, such methods have a problem of dealing with fast motion (FM), motion blur (MB), illumination variation (IV), and drifting caused by occlusion (OCC). To solve this problem, a tracking method that integrates objectness-bounding box regression (O-BBR) model and a scheme based on kernelized correlation filter (KCF) is proposed. The scheme based on KCF is used to improve the tracking performance of FM and MB. For handling drift problem caused by OCC and IV, we propose objectness proposals trained in bounding box regression as prior knowledge to provide candidates and background suppression. Finally, scheme KCF as a base tracker and O-BBR are fused to obtain a state of a target object. Extensive experimental comparisons of the developed tracking method with other state-of-the-art trackers are performed on some of the challenging video sequences. Experimental comparison results show that our proposed tracking method outperforms other state-of-the-art tracking methods in terms of effectiveness, accuracy, and robustness.
AB - Visual tracking is a fundamental problem in computer vision with extensive application domains in surveillance and intelligent systems. Recently, correlation filter-based tracking methods have shown a great achievement in terms of robustness, accuracy, and speed. However, such methods have a problem of dealing with fast motion (FM), motion blur (MB), illumination variation (IV), and drifting caused by occlusion (OCC). To solve this problem, a tracking method that integrates objectness-bounding box regression (O-BBR) model and a scheme based on kernelized correlation filter (KCF) is proposed. The scheme based on KCF is used to improve the tracking performance of FM and MB. For handling drift problem caused by OCC and IV, we propose objectness proposals trained in bounding box regression as prior knowledge to provide candidates and background suppression. Finally, scheme KCF as a base tracker and O-BBR are fused to obtain a state of a target object. Extensive experimental comparisons of the developed tracking method with other state-of-the-art trackers are performed on some of the challenging video sequences. Experimental comparison results show that our proposed tracking method outperforms other state-of-the-art tracking methods in terms of effectiveness, accuracy, and robustness.
KW - Correlation filter
KW - Learning
KW - Objectness proposals
KW - Visual tracking
UR - http://www.scopus.com/inward/record.url?scp=85044672040&partnerID=8YFLogxK
U2 - 10.1117/1.JEI.27.2.023011
DO - 10.1117/1.JEI.27.2.023011
M3 - Article
AN - SCOPUS:85044672040
SN - 1017-9909
VL - 27
JO - Journal of Electronic Imaging
JF - Journal of Electronic Imaging
IS - 2
M1 - 023011
ER -