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

Algorithms

Yanmin Luo¹  Yongzhao Du²  Zhi Chen²  Wancheng Zhang²  Peizhong Liu²  Daxin Zhu³ 

[1] College of Computer Science and Technology, Huaqiao University, Xiamen 361021, China;College of Engineering, Huaqiao University, Quanzhou 362021, China;School of Mathematics and Computer Science, Quanzhou Normal University, Quanzhou 362021, China;

关键词: visual tracking;    discriminative correlation filters;    hierarchical convolutional features;    re-detection;    model update;   

DOI  :  10.3390/a12010008

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown