Depth information aided constrained correlation filter for visual tracking

6th Annual 2018 International Conference on Geo-Spatial Knowledge and Intelligence

Li, Guanqun^1 ; Huang, Lei^1 ; Zhang, Peichang^1 ; Li, Qiang^1 ; Huo, Yongkai^2

Collage of Information Engineering, Shenzhen University, NanHai Ave.3688, NanShan Dist, Shenzhen, China^1

Collage of Computer Science and Software Engineering, Shenzhen University, NanHai Ave.3688, NanShan Dist, Shenzhen, China^2

关键词: Background noise;    Correlation filters;    Depth information;    Discriminability;    Feature weighting;    State-of-the-art performance;    Tracking algorithm;    Visual tracking systems;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/234/1/012005/pdf
DOI  :  10.1088/1755-1315/234/1/012005

来源: IOP

【 摘 要 】

In this paper, we proposed a novel visual tracking system by constructing the Constrained Correlation Filter (CCF) with Depth Information. More specifically, in our proposed system, to avoid the boundary effects and the fixed shape assumption of conventional Discriminative Correlation Filter (DCF), the shape of target is extracted from depth image provided by RGB-D sensor to construct the CCF, which may prevent the filter from being disturbed by the background noise at the learning stage and enlarge the search region. Moreover, in order to avoid the drifting problem, the update of the model is stopped once part of the target is occluded. The feature weighting coefficients, which reflect the discriminability of the feature channels, are used at the location stage to improve the discriminability. The experimental results show that our method is capable of achieving state-of-the-art performance on Princeton RGB-D tracking benchmark among all public tracking algorithms.