Tracking of variable scale object based on compressive sensing

MAO Jian-sen; QU Yu-fu

doi:10.3788/YJYXS20163105.0497

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Tracking of variable scale object based on compressive sensing

Display and Image | 更新时间：2020-08-12

- Tracking of variable scale object based on compressive sensing
- Chinese Journal of Liquid Crystals and Displays Vol. 31, Issue 5, Pages: 497-505(2016)
- 作者机构：
  
  北京航空航天大学仪器科学与光电工程学院, 北京 100191
- 作者简介：
- 基金信息：
  
  Supported by National Natural Science Foundation of China(No.51105027)
- DOI：10.3788/YJYXS20163105.0497
  CLC： TP391
- Received：12 September 2015，
  
  Revised：09 October 2015，
  
  Published：05 May 2016
- 稿件说明：
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毛建森, 屈玉福,. 基于压缩感知的变尺度目标跟踪技术[J]. 液晶与显示, 2016,31(5): 497-505

MAO Jian-sen, QU Yu-fu,. Tracking of variable scale object based on compressive sensing[J]. , 2016,31(5): 497-505
毛建森, 屈玉福,. 基于压缩感知的变尺度目标跟踪技术[J]. 液晶与显示, 2016,31(5): 497-505 DOI： 10.3788/YJYXS20163105.0497.

MAO Jian-sen, QU Yu-fu,. Tracking of variable scale object based on compressive sensing[J]. , 2016,31(5): 497-505 DOI： 10.3788/YJYXS20163105.0497.

摘要

为解决变尺度目标的跟踪问题

本文基于压缩感知理论设计了一种具有目标尺度不变性的目标跟踪方法。该方法首先通过插值的方式将初始帧中要跟踪的目标扩展细化至设定的模板图像大小

提取其压缩感知变换后的低维Haar-like特征作为模板特征并初始化分类器

其次利用卡尔曼滤波对待跟踪的图像帧中目标所在位置和尺度变化趋势进行预测

然后在预测目标所在位置周围提取多个不同尺度的待测目标样本并提取其压缩感知变换后的低维Haar-like特征

最后将这些特征送入分类器进行分类得到真实目标并更新分类器。经过实验验证

本文所设计的跟踪方法的平均跟踪成功率为77%

平均中心位置误差为12像素。能够实现对运动过程中发生尺度变化的目标的有效跟踪。

Abstract

In order to solve the tracking of the variable scale object

a novel tracking algorithm with scale invariance is proposed in this paper. First

the tracked object in the initial frame is expanded to the size of the specified template image by the means of interpolation. Then the low-dimensional Haar-like features of the expanded object are extracted by compressive sensing and used as template features

and the classifier is initialized at the same time. Second

the target's center and scale variation in the frame to be processed are predicted by Kalman filtering. After that

object samples with different scales are collected around the predicted target center above and the low-dimensional Haar-like features of the object samples are extracted by compressive sensing. Finally

the low-dimensional Haar-like features are sent to the classifier to get the tracking target and the classifier is updated at the moment. Experiments show that the average success rate is 77% and the average center location error is 12 pixel. It indicates that the object tracking method proposed above is effective on the tracking of the moving target with scale variation.

关键词

Keywords

references

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