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this document explains the process of training a pedestrian detector using Viola and Jones’
cascaded classifier approach. it was originally
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Adaboost算法简单可靠、学习精度高的特点，提出一种基于Adaboost算法的行人实时检测方法的论文。-Adaboost algorithm is simple, reliable, high precision, the characteristics of study proposed, based on Adaboost algorithm for real-time detection method of pedestrian papers.
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The objective of this paper is to provide an overview of the current state of the art from both methodologicaland experimental perspectives. The first part of the paper consists of a survey. We cover the main components of a pedestrian detection syst
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基于多特征的AdaBoost行人检测算法, 提出一种融合灰
度和轮廓信息的新的多特征综合表示方法. 该方法通过统计样本的权重直方图建立分类模
型, 并用多个直方图的乘积表示样本在多特征下对应的联合概率分布-AdaBoost pedestrian detection algorithm based on multi-feature, presents a fusion of grayscale and contour information of the new multi-feature
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ICCV2013：
简 称UDN算法，从文中描述的检测效果来看，该方法是所有方法中最好的，并且，效果远超过其他方法。经过对论文和该算法源码的研究，该算法是与作者另外一篇 论文的方法 ，另外的论文算法做图片扫描，得到矩形框，然后用该方法对矩形框进行进一步确认，以及降低误警率和漏警率。另外的论文是：Multi-Stage Contextual Deep Learning for Pedestrian Detection
说得难听一点，这篇文章对行人检测没有多大的贡献。仅仅是用深度学习
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