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Pedestrian Detection Inspired by Appearance Constancy and Shape Symmetry.

Authors
  • Cao, Jiale
  • Pang, Yanwei
  • Li, Xuelong
Type
Published Article
Journal
IEEE Transactions on Image Processing
Publisher
Institute of Electrical and Electronics Engineers
Publication Date
Dec 01, 2016
Volume
25
Issue
12
Pages
5538–5551
Identifiers
DOI: 10.1109/TIP.2016.2609807
PMID: 27654480
Source
Medline
License
Unknown

Abstract

Most state-of-the-art methods in pedestrian detection are unable to achieve a good trade-off between accuracy and efficiency. For example, ACF has a fast speed but a relatively low detection rate, while checkerboards have a high detection rate but a slow speed. Inspired by some simple inherent attributes of pedestrians (i.e., appearance constancy and shape symmetry), we propose two new types of non-neighboring features: side-inner difference features (SIDF) and symmetrical similarity features (SSFs). SIDF can characterize the difference between the background and pedestrian and the difference between the pedestrian contour and its inner part. SSF can capture the symmetrical similarity of pedestrian shape. However, it is difficult for neighboring features to have such above characterization abilities. Finally, we propose to combine both non-neighboring features and neighboring features for pedestrian detection. It is found that non-neighboring features can further decrease the log-average miss rate by 4.44%. The relationship between our proposed method and some state-of-the-art methods is also given. Experimental results on INRIA, Caltech, and KITTI data sets demonstrate the effectiveness and efficiency of the proposed method. Compared with the state-of-the-art methods without using CNN, our method achieves the best detection performance on Caltech, outperforming the second best method (i.e., checkerboards) by 2.27%. Using the new annotations of Caltech, it can achieve 11.87% miss rate, which outperforms other methods.

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