Changchun Institute of Optics,Fine Mechanics and Physics,CAS
An Approach to Overcome Occlusions in Visual Tracking By Occlusion Estimating Agency and Self-Adapting Learning Rate for Filter's Training | |
Jiang, K. W.; Qian, F.; Song, C.; Zhang, B. | |
2018 | |
发表期刊 | Ieee Signal Processing Letters
![]() |
ISSN | 1070-9908 |
卷号 | 25期号:12页码:1890-1894 |
摘要 | Visual tracking methods have been successful in recent years. Correlation filter (CF) based methods significantly advanced state-of-the-art tracking. The advancement in CF tracking performance is predominantly attributed to powerful features and sophisticated online learning formulations. However, there would be trouble if the tracker indiscriminately learned samples. Particularly, when the target is severely occluded or out-of-view, the tracker will continuously learn the wrong information, resulting target loss in the following frames. In this study, aiming to avoid incorrect training when occlusions occur, we propose a regional color histogram-based occlusion estimating agency (RCHBOEA), which estimates the occlusion level and then instructs, based on the result, the tracker to work in one of two modes: normal or lost. In the normal mode, an occlusion level-based self-adopting learning rate is used for tracker training. In the lost mode, the tracker pauses its training and conducts a search and recapture strategy on a wider searching area. Our method can easily complement CF-based trackers. In our experiments, we employed four CF-based trackers as a baseline: discriminative CFs (DCF), kernelized CFs (KCF), background-aware CFs (BACF), and efficient convolution operators for tracking: hand-crafted feature version (ECO_HC). We performed extensive experiments on the standard benchmarks: VIVID, OTB50, and OTB100. The results demonstrated that combined with RCHBOEA, the trackers achieved a remarkable improvement. |
关键词 | Overcome occlusion regional color histogram (RCH) self-adopting learning rate visual tracking object tracking benchmark Engineering |
DOI | 10.1109/lsp.2018.2856102 |
收录类别 | SCI ; EI |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ciomp.ac.cn/handle/181722/61223 |
专题 | 中国科学院长春光学精密机械与物理研究所 |
推荐引用方式 GB/T 7714 | Jiang, K. W.,Qian, F.,Song, C.,et al. An Approach to Overcome Occlusions in Visual Tracking By Occlusion Estimating Agency and Self-Adapting Learning Rate for Filter's Training[J]. Ieee Signal Processing Letters,2018,25(12):1890-1894. |
APA | Jiang, K. W.,Qian, F.,Song, C.,&Zhang, B..(2018).An Approach to Overcome Occlusions in Visual Tracking By Occlusion Estimating Agency and Self-Adapting Learning Rate for Filter's Training.Ieee Signal Processing Letters,25(12),1890-1894. |
MLA | Jiang, K. W.,et al."An Approach to Overcome Occlusions in Visual Tracking By Occlusion Estimating Agency and Self-Adapting Learning Rate for Filter's Training".Ieee Signal Processing Letters 25.12(2018):1890-1894. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
An Approach to Overc(2275KB) | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论