Robust Real-time Object Detection
Robust Real-time Object Detection
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DOI：
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发表时间：
2001
影响因子：
19.5
通讯作者：
Paul A. Viola;Michael Jones
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文献类型：
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作者：
Paul A. Viola;Michael Jones
本文描述了一种视觉目标检测框架，该框架能够极快地处理图像，同时实现较高的检测率。有三个关键贡献。首先是引入了一种名为“积分图像”的新图像表示形式，它使我们的探测器所使用的特征能够快速计算。其次是一种基于AdaBoost的学习算法，该算法选择少量关键视觉特征并产生极其高效的分类器[4]。第三个贡献是一种将分类器组合成“级联”的方法，它可以快速丢弃图像的背景区域，同时在有希望的类目标区域上花费更多计算。本文展示了一组在人脸检测领域的实验。该系统产生的人脸检测性能与之前最好的系统[16, 11, 14, 10, 1]相当。在传统台式机上实现时，人脸检测以每秒15帧的速度进行。
作者邮箱：fPaul.Viola,Mike.J.Jonesg@compaq.com
c康柏电脑公司，2001年
这项工作不得以任何商业目的全部或部分复制或转载。在非营利性教育和研究目的的情况下，允许免费全部或部分复制，但所有此类全部或部分副本应包括以下内容：声明此类复制是经位于马萨诸塞州剑桥的康柏电脑公司剑桥研究实验室许可；对作者和该工作的个别贡献者的致谢；以及版权声明的所有适用部分。出于任何其他目的的复制、转载或重新发布都需要向剑桥研究实验室付费获取许可证。保留所有权利。
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康柏电脑公司
剑桥研究实验室
剑桥中心一号
马萨诸塞州剑桥市，02142美国
This paper describes a visual object detection framework that is capable of processing images extremely rapidly while achieving high detection rates. There are three key contributions. The first is the introduction of a new image representation called the “Integral Image” which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features and yields extremely efficient classifiers [4]. The third contribution is a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. A set of experiments in the domain of face detection are presented. The system yields face detection performance comparable to the best previous systems [16, 11, 14, 10, 1]. Implemented on a conventional desktop, face detection proceeds at 15 frames per second. Author email: fPaul.Viola,Mike.J.Jonesg@compaq.com c Compaq Computer Corporation, 2001 This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of the Cambridge Research Laboratory of Compaq Computer Corporation in Cambridge, Massachusetts; an acknowledgment of the authors and individual contributors to the work; and all applicable portions of the copyright notice. Copying, reproducing, or republishing for any other purpose shall require a license with payment of fee to the Cambridge Research Laboratory. All rights reserved. CRL Technical reports are available on the CRL’s web page at http://crl.research.compaq.com. Compaq Computer Corporation Cambridge Research Laboratory One Cambridge Center Cambridge, Massachusetts 02142 USA