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Algorithms 2019, 12(1), 8; https://doi.org/10.3390/a12010008

A Robust Visual Tracking Algorithm Based on Spatial-Temporal Context Hierarchical Response Fusion

1
College of Engineering, Huaqiao University, Quanzhou 362021, China
2
College of Computer Science and Technology, Huaqiao University, Xiamen 361021, China
3
The Key Laboratory for Computer Vision and Pattern Recognition, Huaqiao University, Xiamen 361021, China
4
School of Mathematics and Computer Science, Quanzhou Normal University, Quanzhou 362021, China
*
Author to whom correspondence should be addressed.
Received: 17 October 2018 / Revised: 18 December 2018 / Accepted: 19 December 2018 / Published: 26 December 2018
(This article belongs to the Special Issue Deep Learning for Image and Video Understanding)
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Abstract

Discriminative correlation filters (DCFs) have been shown to perform superiorly in visual object tracking. However, visual tracking is still challenging when the target objects undergo complex scenarios such as occlusion, deformation, scale changes and illumination changes. In this paper, we utilize the hierarchical features of convolutional neural networks (CNNs) and learn a spatial-temporal context correlation filter on convolutional layers. Then, the translation is estimated by fusing the response score of the filters on the three convolutional layers. In terms of scale estimation, we learn a discriminative correlation filter to estimate scale from the best confidence results. Furthermore, we proposed a re-detection activation discrimination method to improve the robustness of visual tracking in the case of tracking failure and an adaptive model update method to reduce tracking drift caused by noisy updates. We evaluate the proposed tracker with DCFs and deep features on OTB benchmark datasets. The tracking results demonstrated that the proposed algorithm is superior to several state-of-the-art DCF methods in terms of accuracy and robustness. View Full-Text
Keywords: visual tracking; discriminative correlation filters; hierarchical convolutional features; re-detection; model update visual tracking; discriminative correlation filters; hierarchical convolutional features; re-detection; model update
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Zhang, W.; Luo, Y.; Chen, Z.; Du, Y.; Zhu, D.; Liu, P. A Robust Visual Tracking Algorithm Based on Spatial-Temporal Context Hierarchical Response Fusion. Algorithms 2019, 12, 8.

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