Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (12): 3565-3570.DOI: 10.11772/j.issn.1001-9081.2021061034
Special Issue: 第十八届中国机器学习会议(CCML 2021)
• The 18th China Conference on Machine Learning • Previous Articles Next Articles
Received:
2021-05-12
Revised:
2021-07-18
Accepted:
2021-07-22
Online:
2021-12-28
Published:
2021-12-10
Contact:
Jing WEN
About author:
LI Qiang, born in 1995, M. S. candidate. His research interests include computer vision, image processing.
Supported by:
通讯作者:
温静
作者简介:
李强(1995—),男,山西大同人,硕士研究生,主要研究方向:计算机视觉、图像处理。
基金资助:
CLC Number:
Jing WEN, Qiang LI. Object tracking algorithm based on spatio-temporal context information enhancement[J]. Journal of Computer Applications, 2021, 41(12): 3565-3570.
温静, 李强. 基于时空上下文信息增强的目标跟踪算法[J]. 《计算机应用》唯一官方网站, 2021, 41(12): 3565-3570.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021061034
算法 | 准确率 | 稳健性 | 预期平均重叠率 |
---|---|---|---|
DaSiamRPN | 0.61 | 0.22 | 0.411 |
SiamRPN | 0.56 | 0.26 | 0.344 |
ATOM | — | — | — |
SiamRPN++ | 0.64 | 0.20 | 0.464 |
SiamMask-box | 0.618 | 0.210 | 0.419 |
SiamMask-MBR | 0.621 | 0.210 | 0.421 |
SiamAsbm-box | 0.631 | 0.218 | 0.425 |
SiamAsbm-MBR | 0.661 | 0.214 | 0.434 |
Tab. 1 Experimental results on VOT2016 dataset
算法 | 准确率 | 稳健性 | 预期平均重叠率 |
---|---|---|---|
DaSiamRPN | 0.61 | 0.22 | 0.411 |
SiamRPN | 0.56 | 0.26 | 0.344 |
ATOM | — | — | — |
SiamRPN++ | 0.64 | 0.20 | 0.464 |
SiamMask-box | 0.618 | 0.210 | 0.419 |
SiamMask-MBR | 0.621 | 0.210 | 0.421 |
SiamAsbm-box | 0.631 | 0.218 | 0.425 |
SiamAsbm-MBR | 0.661 | 0.214 | 0.434 |
算法 | 准确率 | 稳健性 | 预期平均重叠率 |
---|---|---|---|
DaSiamRPN[ | 0.569 | 0.337 | 0.326 |
SiamRPN[ | 0.490 | 0.460 | 0.244 |
ATOM[ | 0.590 | 0.204 | 0.401 |
SiamRPN++[ | 0.600 | 0.234 | 0.414 |
SiamMask-box | 0.589 | 0.300 | 0.360 |
SiamMask-MBR[ | 0.592 | 0.286 | 0.359 |
SiamAsbm-box | 0.592 | 0.295 | 0.364 |
SiamAsbm-MBR | 0.629 | 0.258 | 0.370 |
Tab. 2 Experimental results on VOT2018 dataset
算法 | 准确率 | 稳健性 | 预期平均重叠率 |
---|---|---|---|
DaSiamRPN[ | 0.569 | 0.337 | 0.326 |
SiamRPN[ | 0.490 | 0.460 | 0.244 |
ATOM[ | 0.590 | 0.204 | 0.401 |
SiamRPN++[ | 0.600 | 0.234 | 0.414 |
SiamMask-box | 0.589 | 0.300 | 0.360 |
SiamMask-MBR[ | 0.592 | 0.286 | 0.359 |
SiamAsbm-box | 0.592 | 0.295 | 0.364 |
SiamAsbm-MBR | 0.629 | 0.258 | 0.370 |
Baseline基础上增加的模块 | 准确率 | 稳健性 | 预期平均重叠率 | ||
---|---|---|---|---|---|
特征叠加 | 特征对齐 | 特征增强 | |||
0.592 | 0.286 | 0.359 | |||
√ | 0.589 | 0.300 | 0.354 | ||
√ | √ | 0.579 | 0.272 | 0.360 | |
√ | √ | 0.610 | 0.290 | 0.355 | |
√ | √ | √ | 0.629 | 0.258 | 0.370 |
Tab. 3 Ablation experiment
Baseline基础上增加的模块 | 准确率 | 稳健性 | 预期平均重叠率 | ||
---|---|---|---|---|---|
特征叠加 | 特征对齐 | 特征增强 | |||
0.592 | 0.286 | 0.359 | |||
√ | 0.589 | 0.300 | 0.354 | ||
√ | √ | 0.579 | 0.272 | 0.360 | |
√ | √ | 0.610 | 0.290 | 0.355 | |
√ | √ | √ | 0.629 | 0.258 | 0.370 |
算法 | 区域相似度 | 轮廓精度 | 时间稳定性 | ||||
---|---|---|---|---|---|---|---|
JM | JO | JD | FM | FO | FD | TM | |
Msk[ | 0.792 | 0.924 | 0.094 | 0.749 | 0.864 | 0.093 | 0.222 |
Osvos[ | 0.797 | 0.933 | 0.151 | 0.806 | 0.922 | 0.155 | 0.348 |
SegFlow[ | 0.761 | 0.906 | 0.121 | 0.760 | 0.855 | 0.104 | 0.194 |
SiamMask[ | 0.712 | 0.862 | 0.051 | 0.663 | 0.759 | 0.073 | 0.279 |
本文算法 | 0.714 | 0.854 | 0.049 | 0.666 | 0.751 | 0.071 | 0.279 |
Tab. 4 Experimental results on DAVIS-2016 dataset
算法 | 区域相似度 | 轮廓精度 | 时间稳定性 | ||||
---|---|---|---|---|---|---|---|
JM | JO | JD | FM | FO | FD | TM | |
Msk[ | 0.792 | 0.924 | 0.094 | 0.749 | 0.864 | 0.093 | 0.222 |
Osvos[ | 0.797 | 0.933 | 0.151 | 0.806 | 0.922 | 0.155 | 0.348 |
SegFlow[ | 0.761 | 0.906 | 0.121 | 0.760 | 0.855 | 0.104 | 0.194 |
SiamMask[ | 0.712 | 0.862 | 0.051 | 0.663 | 0.759 | 0.073 | 0.279 |
本文算法 | 0.714 | 0.854 | 0.049 | 0.666 | 0.751 | 0.071 | 0.279 |
算法 | 区域相似度 | 轮廓精度 | 时间稳定性 | ||||
---|---|---|---|---|---|---|---|
JM | JO | JD | FM | FO | FD | TM | |
OnAVOS[ | 0.616 | 0.674 | 0.279 | 0.691 | 0.754 | 0.266 | 0.431 |
Osvos[ | 0.566 | 0.636 | 0.261 | 0.639 | 0.736 | 0.270 | 0.529 |
SiamMask[ | 0.534 | 0.628 | 0.193 | 0.585 | 0.675 | 0.209 | 0.451 |
本文算法 | 0.609 | 0.704 | 0.180 | 0.611 | 0.665 | 0.200 | 0.430 |
Tab. 5 Experimental results on DAVIS-2016 dataset
算法 | 区域相似度 | 轮廓精度 | 时间稳定性 | ||||
---|---|---|---|---|---|---|---|
JM | JO | JD | FM | FO | FD | TM | |
OnAVOS[ | 0.616 | 0.674 | 0.279 | 0.691 | 0.754 | 0.266 | 0.431 |
Osvos[ | 0.566 | 0.636 | 0.261 | 0.639 | 0.736 | 0.270 | 0.529 |
SiamMask[ | 0.534 | 0.628 | 0.193 | 0.585 | 0.675 | 0.209 | 0.451 |
本文算法 | 0.609 | 0.704 | 0.180 | 0.611 | 0.665 | 0.200 | 0.430 |
算法 | 速度 | 算法 | 速度 |
---|---|---|---|
Msk[ | 0.1 | SiamMask[ | 55 |
Osvos[ | 0.1 | 本文算法 | 32 |
Tab. 6 Speed analysis
算法 | 速度 | 算法 | 速度 |
---|---|---|---|
Msk[ | 0.1 | SiamMask[ | 55 |
Osvos[ | 0.1 | 本文算法 | 32 |
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