Knowledge Commons of Institute of Automation,CAS
Feature Distilled Tracking | |
Zhu Guibo1![]() ![]() ![]() ![]() | |
发表期刊 | IEEE Transaction on Cybernetics
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2019-02 | |
卷号 | 49期号:2页码:440 - 452 |
产权排序 | 1 |
摘要 | Feature extraction and representation is one of the most important components for fast, accurate, and robust visual tracking. Very deep convolutional neural networks (CNNs) provide effective tools for feature extraction with good generalization ability. However, extracting features using very deep CNN models needs high performance hardware due to its large computation complexity, which prohibits its extensions in real-time applications. To alleviate this problem, we aim at obtaining small and fast-to-execute shallow models based on model compression for visual tracking. Specifically, we propose a small feature distilled network (FDN) for tracking by imitating the intermediate representations of a much deeper network. The FDN extracts rich visual features with higher speed than the original deeper network. To further speed-up, we introduce a shift-and-stitch method to reduce the arithmetic operations, while preserving the spatial resolution of the distilled feature maps unchanged. Finally, a scale adaptive discriminative correlation filter is learned on the distilled feature for visual tracking to handle scale variation of the target. Comprehensive experimental results on object tracking benchmark datasets show that the proposed approach achieves 5x speed-up with competitive performance to the state-of-the-art deep trackers.
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关键词 | Correlation Filter Model Compression Visual Tracking |
学科领域 | Computer Science, Artificial Intelligence, Cybernetics |
DOI | 10.1109/TCYB.2017.2776977 |
收录类别 | SCI |
语种 | 英语 |
项目资助者 | National Natural Science Foundation of China under Grant 61702510, Grant 61773375, Grant 61370036, Grant 61772277, and Grant 61772527. |
WOS记录号 | WOS:3 |
是否为代表性论文 | 是 |
七大方向——子方向分类 | 图像视频处理与分析 |
国重实验室规划方向分类 | 视觉信息处理 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/22062 |
专题 | 紫东太初大模型研究中心_图像与视频分析 |
通讯作者 | Jinqiao Wang |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences 3.Nanjing Audit University 4.Indiana University School of Medicine |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Zhu Guibo,Jinqiao Wang,Peisong Wang,et al. Feature Distilled Tracking[J]. IEEE Transaction on Cybernetics,2019,49(2):440 - 452. |
APA | Zhu Guibo,Jinqiao Wang,Peisong Wang,Yi Wu,&Hanqing Lu.(2019).Feature Distilled Tracking.IEEE Transaction on Cybernetics,49(2),440 - 452. |
MLA | Zhu Guibo,et al."Feature Distilled Tracking".IEEE Transaction on Cybernetics 49.2(2019):440 - 452. |
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 | ||
feature distilled tr(2450KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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