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A Rapidly Trainable and Global Illumination Invariant Object Detection System

机译:快速可训练的全局照明不变物体检测系统

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摘要

This paper addresses the main difficulty in adopting Viola-Jones-type object detection systems: their training time. Large training times are the result of having to repeatedly evaluate thousands of Haar-like features (HFs) in a database of object and clutter class images. The proposed object detector is fast to train mainly because of three reasons. Firstly, classifiers that exploit a clutter (non-object) model are used to build the object detector and, hence, they do not need to evaluate clutter images during training. Secondly, the redundant HFs are heuristically pre-eliminated from the feature pool to obtain a small set of independent features. Thirdly, classifiers that have fewer parameters to be optimized are used to build the object detector. As a result, they are faster to train than their traditional counterparts. Apart from faster training, an additional advantage of the proposed detector is that its output is invariant to global illumination changes. Our results indicate that if the object class does not exhibit substantial intra-class variation, then the proposed method can be used to build accurate and real-time object detectors whose training time is in the order of seconds. The quick training and testing speed of the proposed system makes it ideal for use in content-based image retrieval applications.
机译:本文解决了采用Viola-Jones型物体检测系统的主要困难:它们的训练时间。大量的训练时间是必须反复评估对象和混乱类图像数据库中成千上万个类似Haar的特征(HF)的结果。提出的物体检测器快速训练,主要是由于三个原因。首先,利用杂波(非对象)模型的分类器用于构建目标检测器,因此,它们无需在训练过程中评估杂波图像。其次,从特征库中启发式地预先消除了冗余HF,以获得少量独立特征。第三,使用具有较少要优化参数的分类器来构建目标检测器。结果,他们的培训速度比传统的同行更快。除了训练更快以外,所提出的探测器的另一个优点是其输出对于全局照度变化是不变的。我们的结果表明,如果对象类别没有显着的类内变化,则所提出的方法可用于构建训练时间在几秒钟左右的准确且实时的对象检测器。拟议系统的快速培训和测试速度使其非常适合用于基于内容的图像检索应用程序。

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  • 来源
  • 会议地点 Guadalajara Jalisco(MX);Guadalajara Jalisco(MX)
  • 作者单位

    Research Group for Computational Imaging Simulation Technologies in Biomedicine, Universitat Pompeu Fabra, Barcelona, Spain Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Spain;

    rnResearch Group for Computational Imaging Simulation Technologies in Biomedicine, Universitat Pompeu Fabra, Barcelona, Spain Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Spain;

    rnResearch Group for Computational Imaging Simulation Technologies in Biomedicine, Universitat Pompeu Fabra, Barcelona, Spain Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Spain Catalan Institution for Research and Advanced Studies (ICREA), Spain;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 信息处理(信息加工);
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