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电子学报 ›› 2012, Vol. 40 ›› Issue (4): 814-820.DOI: 10.3969/j.issn.0372-2112.2012.04.031

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行人检测技术综述

苏松志1,2, 李绍滋1,2, 陈淑媛3, 蔡国榕1,2,4, 吴云东4   

  1. 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
  • 收稿日期:2010-04-07 修回日期:2011-06-30 出版日期:2012-04-25 发布日期:2012-04-25

A Survey on Pedestrian Detection

SU Song-zhi1,2, LI Shao-zi1,2, CHEN Shu-yuan3, CAI Guo-rong1,2,4, WU Yun-dong4   

  1. 1. School of Information Science and Technology,Xiamen University,Xiamen,Fujian 361005 China;2. Fujian Key Laboratory of the Brain-like Intelligent Systems (Xiamen University),Xiamen,Fujian 361005,China;3. Department of Computer Engineering and Science,Yuan-Ze University,Taiwan,China;4. School of Science,Jimei University,Xiamen,Fujian 361021,China
  • Received:2010-04-07 Revised:2011-06-30 Online:2012-04-25 Published:2012-04-25

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摘要： 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.

关键词: 行人检测, 目标检测, 智能监控, 车辆辅助驾驶

Abstract: 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.

Key words: pedestrian detection, object detection, intelligent surveillance, driver assistance systems

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