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首页> 外文期刊>Neurocomputing >Pedestrian detection in unseen scenes by dynamically updating visual words
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Pedestrian detection in unseen scenes by dynamically updating visual words

机译：通过动态更新视觉单词在看不见的场景中进行行人检测

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摘要

Adapting trained detectors to unseen scenes is a critical problem in pedestrian detection. The performance of trained detector may drop quickly when scenes vary significantly. Retraining a detector with labeled samples from the new scenes may improve its performance. However, it is difficult to obtain enough labeled samples in real applications. In this paper, a novel bag of visual words based method is proposed to detect pedestrians in unseen scenes by dynamically updating the key words. The proposed method achieves its adaptability by using three strategies covering key word selection, detector invariance, and codebook update: (1) In order to select typical words representing pedestrians, a low dimensional model of visual words is built to describe their distribution and select key words using manifold learning. (2) Matching confidence vector (MCV), a novel visual words measurement is proposed, which aims to generate a uniform input vector for the fixed detector applied to different pedestrian codebooks. (3) When detecting pedestrians under changing road conditions, the key word set will be dynamically adjusted according to the matching frequency of each word to adapt the detector to the new scenes. By employing the above strategies, the proposed method is able to detect pedestrians in different scenes without retraining the detector. Experiments in different scenes showed that our proposed method can achieve better adaptability to various scenes and get better performance than other existing methods in unseen scenes.
机译：使训练有素的探测器适应看不见的场景是行人探测中的关键问题。当场景变化很大时，训练有素的探测器的性能可能会迅速下降。使用来自新场景的带标签样本对检测器进行重新训练可以提高其性能。但是，在实际应用中很难获得足够的标记样品。本文提出了一种新颖的基于视觉词袋的方法，通过动态更新关键词来检测看不见的场景中的行人。所提出的方法通过使用三种策略来实现其自适应性，这些策略包括关键词选择，检测器不变性和码本更新：（1）为了选择代表行人的典型词，建立了低维视觉词模型来描述他们的分布并选择密钥使用流形学习的单词。 （2）匹配置信向量（MCV），提出了一种新颖的视觉单词测量方法，旨在为应用于不同行人密码本的固定检测器生成统一的输入向量。 （3）在变化的道路条件下检测行人时，将根据每个单词的匹配频率动态调整关键字集，以使检测器适应新的场景。通过采用上述策略，所提出的方法能够在不重新训练检测器的情况下检测不同场景中的行人。在不同场景下的实验表明，与其他现有方法相比，我们提出的方法可以更好地适应各种场景，并获得更好的性能。

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  • 来源
    《Neurocomputing》 |2013年第7期|232-242|共11页
  • 作者

    Xianbin Cao; Li Wang; Bo Ning; Yuan Yuan; Pingkun Yan;

  • 作者单位

    The Beihang University, Beijing 100191, PR China;

    The Beihang University, Beijing 100191, PR China;

    University of Science and Technology of China,Hefei 230026, PR China;

    The Center for OPTical IMagery Analysis and Learning (OPTIMAL), State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, Shaanxi, PR China;

    The Center for OPTical IMagery Analysis and Learning (OPTIMAL), State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, Shaanxi, PR China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Pedestrian detection; Adaptive detector; Bag of visual words; Manifold leaning;

    机译：行人检测;自适应检测器视觉单词袋;歧管倾斜;

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