Visual tracking using objectness-bounding box regression and correlation filters

Jimmy T. Mbelwa; Qingjie Zhao; Yao Lu; Fasheng Wang; Mercy E. Mbise

doi:10.1117/1.JEI.27.2.023011

21 March 2018 Visual tracking using objectness-bounding box regression and correlation filters

Jimmy T. Mbelwa, Qingjie Zhao, Yao Lu, Fasheng Wang, Mercy E. Mbise

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 2, 023011 (March 2018). https://doi.org/10.1117/1.JEI.27.2.023011

Abstract

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.

Citation Download Citation

Jimmy T. Mbelwa, Qingjie Zhao, Yao Lu, Fasheng Wang, and Mercy E. Mbise "Visual tracking using objectness-bounding box regression and correlation filters," Journal of Electronic Imaging 27(2), 023011 (21 March 2018). https://doi.org/10.1117/1.JEI.27.2.023011

Received: 18 October 2017; Accepted: 15 February 2018; Published: 21 March 2018

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