1. 厦门大学信息科学与技术学院,福建,厦门,361005
2. 厦门大学福建省仿脑智能系统重点实验室,福建,厦门,361005
3. 元智大学资讯工程系,台湾
4. 集美大学理学院,福建,厦门,361021
5. 厦门大学信息科学与技术学院福建厦门,361005
6. 厦门大学福建省仿脑智能系统重点实验室福建厦门,361005
7. 元智大学资讯工程系台湾
8. 集美大学理学院福建厦门,361021
纸质出版:2012
移动端阅览
苏松志, 李绍滋, 陈淑媛, 等. 行人检测技术综述[J]. 电子学报, 2012,40(4):814-820.
SU Song-zhi, LI Shao-zi, CHEN Shu-yuan, et al. A Survey on Pedestrian Detection[J]. Acta Electronica Sinica, 2012, 40(4): 814-820.
苏松志, 李绍滋, 陈淑媛, 等. 行人检测技术综述[J]. 电子学报, 2012,40(4):814-820. DOI: 10.3969/j.issn.0372-2112.2012.04.031.
SU Song-zhi, LI Shao-zi, CHEN Shu-yuan, et al. A Survey on Pedestrian Detection[J]. Acta Electronica Sinica, 2012, 40(4): 814-820. DOI: 10.3969/j.issn.0372-2112.2012.04.031.
行人检测是计算机视觉中的研究热点和难点
本文对2005-2011这段时间内的行人检测技术中最核心的两个问题—特征提取、分类器与定位—的研究现状进行综述.文章中首先将这些问题的处理方法分为不同的类别
将行人特征分为底层特征、基于学习的特征和混合特征
分类与定位方法分为滑动窗口法和超越滑动窗口法
并从纵横两个方向对这些方法的优缺点进行分析和比较
然后总结了构建行人检测器在实现细节上的一些经验
最后对行人检测技术的未来进行展望.
Pedestrian detection is an active area of research with challenge in computer vision.This study conducts a detailed survey on state-of-the-art pedestrian detection methods from 2005 to 2011
focusing on the two most important problems:feature extraction
the classification and localization.We divided these methods into different categories;pedestrian features are divided into three subcategories:low-level feature
learning-based feature and hybrid feature.On the other hand
classification and localization is also divided into two sub-categories:sliding window and beyond sliding window.According to the taxonomy
the pros and cons of different approaches are discussed.Finally
some experiences of how to construct a robust pedestrian detector are presented and future research trends are proposed.
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