Pedestrian Detection Based on Fast R-CNN and Batch Normalization View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2017

AUTHORS

Zhong-Qiu Zhao , Haiman Bian , Donghui Hu , Wenjuan Cheng , Hervé Glotin

ABSTRACT

Most of the pedestrian detection methods are based on hand-crafted features which produce low accuracy on complex scenes. With the development of deep learning method, pedestrian detection has achieved great success. In this paper, we take advantage of a convolutional neural network which is based on Fast R-CNN framework to extract robust pedestrian features for efficient and effective pedestrian detection in complicated environments. We use the EdgeBoxes algorithm to generate effective region proposals from an image, as the quality of extracted region proposals can greatly affect the detection performance. In order to reduce the training time and to improve the generalization performance, we add a batch normalization layer between the convolutional layer and the activation function layer. Experiments show that the proposed method achieves satisfactory performance on the INRIA and ETH datasets. More... »

PAGES

735-746

References to SciGraph publications

  • 2004-05. Robust Real-Time Face Detection in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2015. Pedestrian Detection with Deep Convolutional Neural Network in COMPUTER VISION - ACCV 2014 WORKSHOPS
  • 2014. Strengthening the Effectiveness of Pedestrian Detection with Spatially Pooled Features in COMPUTER VISION – ECCV 2014
  • 2014. Edge Boxes: Locating Object Proposals from Edges in COMPUTER VISION – ECCV 2014
  • 2015. Plant Leaf Identification via a Growing Convolution Neural Network with Progressive Sample Learning in COMPUTER VISION -- ACCV 2014
  • 2013-09. Selective Search for Object Recognition in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2005-07. Detecting Pedestrians Using Patterns of Motion and Appearance in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • Book

    TITLE

    Intelligent Computing Theories and Application

    ISBN

    978-3-319-63308-4
    978-3-319-63309-1

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-63309-1_65

    DOI

    http://dx.doi.org/10.1007/978-3-319-63309-1_65

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1090803775


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