Visual Object Tracking Algorithm Based on Foreground Optimization

XIE Qing-song; LIU Xiao-qing; AN Zhi-yong; LI Bo

doi:10.12263/DZXB.20210641

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Visual Object Tracking Algorithm Based on Foreground Optimization

PAPERS | 更新时间：2025-12-08

- Visual Object Tracking Algorithm Based on Foreground Optimization
- ACTA ELECTRONICA SINICA Vol. 50, Issue 7, Pages: 1558-1566(2022)
- 作者机构：
  
  1.山东工商学院计算机科学与技术学院，山东烟台 264000
  2.山东工商学院信息与电子工程学院，山东烟台 264000
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20210641
  CLC： TP391.4
- Received：19 May 2021，
  
  Revised：2021-10-07，
  
  Published：25 July 2022
- 稿件说明：
移动端阅览
谢青松,刘晓庆,安志勇等.基于前景优化的视觉目标跟踪算法[J].电子学报,2022,50(07):1558-1566.

XIE Qing-song,LIU Xiao-qing,AN Zhi-yong,et al.Visual Object Tracking Algorithm Based on Foreground Optimization[J].ACTA ELECTRONICA SINICA,2022,50(07):1558-1566.
谢青松,刘晓庆,安志勇等.基于前景优化的视觉目标跟踪算法[J].电子学报,2022,50(07):1558-1566. DOI： 10.12263/DZXB.20210641.

XIE Qing-song,LIU Xiao-qing,AN Zhi-yong,et al.Visual Object Tracking Algorithm Based on Foreground Optimization[J].ACTA ELECTRONICA SINICA,2022,50(07):1558-1566. DOI： 10.12263/DZXB.20210641.

摘要

将目标分割技术引入跟踪领域是当前的研究热点.目前，基于分割的跟踪算法往往根据分割结果计算最小外接矩形，以此作为跟踪框，但复杂的目标运动使得跟踪框内包含较多背景，从而导致精度下降.针对该问题，本文提出了一种基于前景优化的视觉目标跟踪算法，将跟踪框的尺度和角度优化统一于前景优化框架中.首先评估跟踪框内的前景比例，若小于设定阈值，则对跟踪框分别进行尺度和角度优化；在尺度优化模块中，结合回归框计算跟踪框的条件概率，根据条件概率的结果分情形进行尺度优化；角度优化模块中，针对跟踪框设定多个偏移角度，利用前景IoU（Intersection over Union）极大策略选择最优跟踪框角度.结果证明，将本文方法应用于SiamMask算法，精度在VOT2016，VOT2018和VOT2019数据集分别提升约3.2%，3.7%和3.6%，而EAO分别提升约1.8%，1.9%和1.6%.另外，本文的方法针对基于分割的跟踪算法具有一定的普适性.

Abstract

The introduction of object segmentation technology into the tracking field is a current research hotspot. At present

the tracking algorithm based on segmentation often calculates the minimum bounding rectangle as the bounding box according to the segmentation result. However

the complex target movement makes the bounding box contain more background

which leads to a decrease in accuracy. In response to the problem

this paper proposes a visual object tracking algorithm based on foreground optimization

which unifies the optimization of the scale and angle in the bounding box into the foreground optimization frame. First

the foreground ratio in the bounding box is evaluated. If it is less than the set threshold

the scale and angle of the bounding box are optimized; in the scale optimization module

the conditional probability of the bounding box is calculated in combination with the regression box

and the scale is optimized according to the results of the conditional probability; in the angle optimization module

many deviation angles are set for the bounding box

and the optimal bounding box angle is chosen by the foreground IoU (Intersection over Union) maximum strategy. The proposed method is applied to the SiamMask algorithm. Results show that the accuracy is improved by about 3.2%

3.7% and 3.6% in the VOT2016

VOT2018 and VOT2019 data sets

respectively

while EAO is increased by about 1.8%

1.9% and 1.6%

respectively. Moreover

our method has a certain universality for segmentation-based tracking algorithms.

关键词

Keywords

references

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