TLD particle swarm optimization target tracking using a sample deletion mechanism

Si-qiu GUO; Tao ZHANG; Xi-kui MIAO

doi:10.3788/OPE.20192705.1206

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TLD particle swarm optimization target tracking using a sample deletion mechanism

Information Sciences | 更新时间：2020-08-13

- TLD particle swarm optimization target tracking using a sample deletion mechanism
- Optics and Precision Engineering Vol. 27, Issue 5, Pages: 1206-1217(2019)
- 作者机构：
  
  1.中国科学院长春光学精密机械与物理研究所，吉林长春 130033
  2.中国科学院大学，北京 100049
  3.中国洛阳电子装备试验中心光电对抗测试评估技术重点实验室，河南洛阳 471000
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20192705.1206
  CLC： TP394.1
- Received：05 December 2018，
  
  Accepted：03 January 2019，
  
  Published：15 May 2019
- 稿件说明：
移动端阅览
Si-qiu GUO, Tao ZHANG, Xi-kui MIAO. TLD particle swarm optimization target tracking using a sample deletion mechanism[J]. Optics and precision engineering, 2019, 27(5): 1206-1217.
DOI：

Si-qiu GUO, Tao ZHANG, Xi-kui MIAO. TLD particle swarm optimization target tracking using a sample deletion mechanism[J]. Optics and precision engineering, 2019, 27(5): 1206-1217. DOI： 10.3788/OPE.20192705.1206.

摘要

为提高TLD算法在广泛场景下跟踪鲁棒性和实时性的问题

本文从跟踪模块和学习模块两个方面对TLD算法进行了改进，提出引入样本删除机制的TLD粒子群目标跟踪算法。首先，用基于颜色特征的粒子群目标跟踪算法替代TLD算法中原来的跟踪模块，增强TLD算法在应对目标出现非刚性形变、尺度变化、旋转、遮挡等情况下的跟踪鲁棒性。接着，针对TLD算法的学习模块引入样本删除机制，在跟踪过程中为样本库中正负样本数量分别设定一个阈值，当正负样本数都达到各自阈值时，便会启动样本删除机制。然后，对待分类进入样本库的图像块进行等级评价，删除对正负样本表征能力都较弱图像块。最后，将样本库中的正负样本与当前目标进行相似度匹配，删除对当前目标表征能力低的样本。通过对OTB2013和OTB2015数据集中相关视频序列的实验结果证明，本文算法的OPE精确度达到0.687，算法的OPE成功率为0.488，算法运算效率平均提高了25.71%。基本满足广泛场景下目标跟踪的鲁棒性，并显著了提高算法运算效率。

Abstract

This study improved the tracking robustness and real-time performance of a tracking-learning-detection (TLD) algorithm for a wide range of scenarios by considering two important aspects

namely

the tracking and learning modules. The study proposed a TLD particle swarm optimization (PSO) target-tracking algorithm using a sample deletion mechanism. First

the original tracking module of a TLD algorithm was replaced by a color-feature-based PSO target-tracking algorithm

which enhanced the tracking performance of the TLD algorithm in terms of target non-rigid deformation

scale variation

rotation

and occlusion. Second

a sample deletion mechanism for the learning module of the TLD algorithm was introduced. During the tracking process

a threshold was set for the positive and negative samples. When both the positive and negative samples reach their respective thresholds

the sample deletion mechanism was initiated. The image blocks to be classified into the sample library were then graded

and those with a weak representation ability for both positive and negative samples were deleted. Finally

we matched the positive and negative samples in the sample library with the current target and delete the samples with low representational ability to the current target. Experiments on OTB2013 and OTB2015 datasets show that the one-pass evaluation (OPE) accuracy of the proposed algorithm reaches 0.687

the OPE success rate of the algorithm is 0.488

and the operation efficiency is improved by 25.64% on average. This essentially satisfies the robustness of target tracking in a wide range of scenarios and significantly improves the computational efficiency of the algorithm.

关键词

Keywords

references

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