基于RGBD和稀疏学习的鲁棒目标跟踪

马子昂; 项志宇

doi:10.1631/FITEE.1601338

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基于RGBD和稀疏学习的鲁棒目标跟踪

常规文章 | Updated：2022-05-19

- 基于RGBD和稀疏学习的鲁棒目标跟踪
- Robust object tracking with RGBD-based sparse learning
- 信息与电子工程前沿（英文） 2017年18卷第7期页码：989-1001
- Affiliations：
  
  College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China
  Zhejiang Provincial Key Laboratory of Information Network Technology, Hangzhou 310027, China
- Author bio：
  
  Zhi-yu XIANG, E-mail: xiangzy@zju.edu.cn
- Funds：
  
  Project supported by the National Natural Science Foundation of China (No. 61571390) and the Fundamental Research Funds for the Central Universities, China (No. 2016QNA5004)
- DOI：10.1631/FITEE.1601338
  中图分类号： TP391
- 收稿：2016-06-14，
  
  修回：2017-;7-13，
  
  纸质出版：2017-07
- Accepted：
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马子昂, 项志宇. 基于RGBD和稀疏学习的鲁棒目标跟踪[J]. 信息与电子工程前沿（英文）, 2017,18(7):989-1001.

Zi-ang MA, Zhi-yu XIANG. Robust object tracking with RGBD-based sparse learning[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(7): 989-1001.
马子昂, 项志宇. 基于RGBD和稀疏学习的鲁棒目标跟踪[J]. 信息与电子工程前沿（英文）, 2017,18(7):989-1001. DOI： 10.1631/FITEE.1601338.

Zi-ang MA, Zhi-yu XIANG. Robust object tracking with RGBD-based sparse learning[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(7): 989-1001. DOI： 10.1631/FITEE.1601338.

摘要

鲁棒目标跟踪近年来成为计算机视觉领域一项重要的且极具挑战性的研究方向。随着深度传感器的普及，深度信息因其对光照变化与遮挡表现出一定的鲁棒性而被广泛应用于视觉目标跟踪算法中。本文提出了一种基于RGBD和稀疏学习的跟踪算法，从三个方面将深度信息应用到稀疏学习跟踪框架。首先将深度图像特征结合现有的基于彩色图像的视觉特征用于目标外观的鲁棒特征描述。为了适应跟踪过程中的各种遮挡情况，我们设计了一种特殊的遮挡物模板用于增广现有的超完备字典。最后，我们进一步提出了一种基于深度信息的遮挡物检测方法用于有效地指示模板更新。基于KITTI和Princeton数据集的大量实验证明了所提出算法的跟踪效果优于时下最先进的多种跟踪器，包括基于稀疏学习的跟踪以及基于RGBD的跟踪。

Abstract

Robust object tracking has been an important and challenging research area in the field of computer vision for decades. With the increasing popularity of affordable depth sensors

range data is widely used in visual tracking for its ability to provide robustness to varying illumination and occlusions. In this paper

a novel RGBD and sparse learning based tracker is proposed. The range data is integrated into the sparse learning framework in three respects. First

an extra depth view is added to the color image based visual features as an independent view for robust appearance modeling. Then

a special occlusion template set is designed to replenish the existing dictionary for handling various occlusion conditions. Finally

a depth-based occlusion detection method is proposed to efficiently determine an accurate time for the template update. Extensive experiments on both KITTI and Princeton data sets demonstrate that the proposed tracker outperforms the state-of-the-art tracking algorithms

including both sparse learning and RGBD based methods.

关键词

Keywords

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