Robust visual tracking based on deep convolutional neural networks and kernelized correlation filters

Hua Yang; Donghong Zhong; Chenyi Liu; Kaiyou Song; Zhouping Yin

doi:10.1117/1.JEI.27.2.023008

16 March 2018 Robust visual tracking based on deep convolutional neural networks and kernelized correlation filters

Hua Yang, Donghong Zhong, Chenyi Liu, Kaiyou Song, Zhouping Yin

Author Affiliations +

Funded by: National Natural Science Foundation of China (NSFC), National Science Foundation of China, Natural Science Foundation of China, Major Project Foundation of Hubei Province, Technology Innovation Project of Hubei

Journal of Electronic Imaging, Vol. 27, Issue 2, 023008 (March 2018). https://doi.org/10.1117/1.JEI.27.2.023008

Abstract

Object tracking is still a challenging problem in computer vision, as it entails learning an effective model to account for appearance changes caused by occlusion, out of view, plane rotation, scale change, and background clutter. This paper proposes a robust visual tracking algorithm called deep convolutional neural network (DCNNCT) to simultaneously address these challenges. The proposed DCNNCT algorithm utilizes a DCNN to extract the image feature of a tracked target, and the full range of information regarding each convolutional layer is used to express the image feature. Subsequently, the kernelized correlation filters (CF) in each convolutional layer are adaptively learned, the correlation response maps of that are combined to estimate the location of the tracked target. To avoid the case of tracking failure, an online random ferns classifier is employed to redetect the tracked target, and a dual-threshold scheme is used to obtain the final target location by comparing the tracking result with the detection result. Finally, the change in scale of the target is determined by building scale pyramids and training a CF. Extensive experiments demonstrate that the proposed algorithm is effective at tracking, especially when evaluated using an index called the overlap rate. The DCNNCT algorithm is also highly competitive in terms of robustness with respect to state-of-the-art trackers in various challenging scenarios.

Citation Download Citation

Hua Yang, Donghong Zhong, Chenyi Liu, Kaiyou Song, and Zhouping Yin "Robust visual tracking based on deep convolutional neural networks and kernelized correlation filters," Journal of Electronic Imaging 27(2), 023008 (16 March 2018). https://doi.org/10.1117/1.JEI.27.2.023008

Received: 18 October 2017; Accepted: 22 February 2018; Published: 16 March 2018

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