Exploiting a depth context model in visual tracking with correlation filter

Zhao-yun CHEN; Lei LUO; Da-fei HUANG; Mei WEN; Chun-yuan ZHANG

doi:10.1631/FITEE.1500389

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Exploiting a depth context model in visual tracking with correlation filter

Regular Papers | Updated：2022-05-19

- Exploiting a depth context model in visual tracking with correlation filter
- 相关滤波视觉跟踪方法中深度上下文模型的研究
- Frontiers of Information Technology & Electronic Engineering Vol. 18, Issue 5, Pages: 667-679(2017)
- Affiliations：
  
  1College of Computer, National University of Defense Technology, Changsha 410073, China
  2National Key Laboratory of Parallel and Distributed Processing, Changsha 410073, China
- Author bio：
  
  E-mail:chenzhaoyun@nudt.edu.cn
  E-mail:l.luo@nudt.edu.cn
- Funds：
- DOI：10.1631/FITEE.1500389
  CLC： TP391.4
- Received：10 November 2015，
  
  Accepted：06 June 2016，
  
  Published：2017-05
- Accepted：
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Zhao-yun CHEN, Lei LUO, Da-fei HUANG, et al. Exploiting a depth context model in visual tracking with correlation filter[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(5): 667-679.
DOI：

Zhao-yun CHEN, Lei LUO, Da-fei HUANG, et al. Exploiting a depth context model in visual tracking with correlation filter[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(5): 667-679. DOI： 10.1631/FITEE.1500389.

摘要

近来，基于相关滤波器的跟踪器因具有较高的计算效率而颇受关注，但这一方法不能很好地处理遮挡和尺度变化。本文旨在将深度信息整合到基于相关滤波器的跟踪器中，以解决跟踪器在上述两种情况下的跟踪失败。同时利用RGB-D数据构建了一个深度上下文模型，用来描述目标与周边区域之间的空间相关性。此外，本文采用了区域生长法使跟踪器对遮挡和尺度变化的场景具有更高鲁棒性，并利用模型更新等优化方法来改进较长视频序列的性能。通过对极具挑战性的基准图像序列测试集的定性和定量评估，本文提出的跟踪器比最先进的算法具有更好的性能。

Abstract

Recently correlation filter based trackers have attracted considerable attention for their high computational efficiency. However

they cannot handle occlusion and scale variation well enough. This paper aims at preventing the tracker from failure in these two situations by integrating the depth information into a correlation filter based tracker. By using RGB-D data

we construct a depth context model to reveal the spatial correlation between the target and its surrounding regions. Furthermore

we adopt a region growing method to make our tracker robust to occlusion and scale variation. Additional optimizations such as a model updating scheme are applied to improve the performance for longer video sequences. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed tracker performs favourably against state-of-the-art algorithms.

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references

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