Object Tracking by Compressive Distribution Fields with Adaptive Hierarchical Structure

WANG Ya-wen; CHEN Hong-chang; LI Shao-mei; GAO Chao

doi:10.3969/j.issn.0372-2112.2017.04.005

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Object Tracking by Compressive Distribution Fields with Adaptive Hierarchical Structure

更新时间：2025-07-16

- Object Tracking by Compressive Distribution Fields with Adaptive Hierarchical Structure
- Acta Electronica Sinica Vol. 45, Issue 4, Pages: 799-804(2017)
- 作者机构：
  
  国家数字交换系统工程技术研究中心,河南,郑州,450002
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61379151, No.61521003);National Key Technology Research and Development Program of the Ministry of Science and Technology (No.2014BAH30B01);Excellent Youth Foundation of He’nan Scientific Committee (No.144100510001)
- DOI：10.3969/j.issn.0372-2112.2017.04.005
  CLC： TP391.41
- Published Online：25 April 2017，
  
  Published：2017
- 稿件说明：
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WANG Ya-wen, CHEN Hong-chang, LI Shao-mei, et al. Object Tracking by Compressive Distribution Fields with Adaptive Hierarchical Structure[J]. Acta Electronica Sinica, 2017, 45(4): 799-804.
DOI：

WANG Ya-wen, CHEN Hong-chang, LI Shao-mei, et al. Object Tracking by Compressive Distribution Fields with Adaptive Hierarchical Structure[J]. Acta Electronica Sinica, 2017, 45(4): 799-804. DOI： 10.3969/j.issn.0372-2112.2017.04.005.

摘要

为了提高分布场跟踪算法的运算效率，增强其在复杂背景下的鲁棒性，提出基于自适应分层结构的压缩分布场跟踪算法.该方法充分考虑目标区域像素值分布情况，引入k-means算法对首帧标记的目标区域进行聚类分析，根据聚类结果自适应的产生分布场结构.针对分布场模型维数较高的缺点，融合压缩感知方法对分布场进行压缩，降低模型维数，提高算法效率.此外，改变原始分布场跟踪算法采用的局部搜索跟踪策略，利用随机抽样的方式来提高算法跟踪精度.实验结果表明，提出的算法与当前流行的跟踪算法相比，具有更好的表现.

Abstract

In order to improve the efficiency of tracking algorithm based on distribution fields and the robustness of the algorithm under complex background

tracking algorithm by compressive distribution fields with adaptive hierarchical structure is presented.Distribution of pixel values in target region is considered in this method

k-means algorithm is introduced to analyse the distribution of pixel values in the first frame

adaptive hierarchical structure of distribution fields is built according to the clustering results.For the problem that the dimension of distribution field model is high

compressive sensing is combined to compress distribution fields

which can reduce the model dimension and improve the efficiency of tracking algorithm.Furthermore

local search strategy in original distribution fields tracking algorithm is changed

random sampling is used to improve the tracking accuracy.Experimental results demonstrate that the proposed algorithm outperforms the state-of-the-art tracking algorithms.

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