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An Occlusion-robust Feature Selection Framework in Pedestrian Detection

Zhixin Guo UGent, Wenzhi Liao UGent, Yifan Xiao, Peter Veelaert UGent and Wilfried Philips UGent (2018) Sensors. 18(7).
abstract
Better features have been driving the progress of pedestrian detection over the past years. However, as features become richer and higher dimensional, noise and redundancy in the feature sets become bigger problems. These problems slow down learning and can even reduce the performance of the learned model. Current solutions typically exploit dimension reduction techniques. In this paper, we propose a simple but effective feature selection framework for pedestrian detection. Moreover, we introduce occluded pedestrian samples into the training process and combine it with a new feature selection criterion, which enables improved performances for occlusion handling problems. Experimental results on the Caltech Pedestrian dataset demonstrate the efficiency of our method over the state-of-art methods, especially for the occluded pedestrians.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
keyword
pedestrian detection, feature selection, occlusion handling, deep learning
journal title
Sensors
volume
18
issue
7
pages
18 pages
ISSN
1424-8220
DOI
10.3390/s18072272
language
English
UGent publication?
yes
classification
U
id
8568925
handle
http://hdl.handle.net/1854/LU-8568925
date created
2018-07-10 09:36:31
date last changed
2018-07-13 14:58:34
@article{8568925,
  abstract     = {Better features have been driving the progress of pedestrian detection over the past years.
However, as features become richer and higher dimensional, noise and redundancy in the feature sets
become bigger problems. These problems slow down learning and can even reduce the performance
of the learned model. Current solutions typically exploit dimension reduction techniques. In this
paper, we propose a simple but effective feature selection framework for pedestrian detection.
Moreover, we introduce occluded pedestrian samples into the training process and combine it with
a new feature selection criterion, which enables improved performances for occlusion handling
problems. Experimental results on the Caltech Pedestrian dataset demonstrate the efficiency of our
method over the state-of-art methods, especially for the occluded pedestrians.},
  author       = {Guo, Zhixin and Liao, Wenzhi and Xiao, Yifan and Veelaert, Peter and Philips, Wilfried},
  issn         = {1424-8220},
  journal      = {Sensors},
  keyword      = {pedestrian detection,feature selection,occlusion handling,deep learning},
  language     = {eng},
  number       = {7},
  pages        = {18},
  title        = {An Occlusion-robust Feature Selection Framework in Pedestrian Detection},
  url          = {http://dx.doi.org/10.3390/s18072272},
  volume       = {18},
  year         = {2018},
}

Chicago
Guo, Zhixin, Wenzhi Liao, Yifan Xiao, Peter Veelaert, and Wilfried Philips. 2018. “An Occlusion-robust Feature Selection Framework in Pedestrian Detection.” Sensors 18 (7).
APA
Guo, Zhixin, Liao, W., Xiao, Y., Veelaert, P., & Philips, W. (2018). An Occlusion-robust Feature Selection Framework in Pedestrian Detection. Sensors, 18(7).
Vancouver
1.
Guo Z, Liao W, Xiao Y, Veelaert P, Philips W. An Occlusion-robust Feature Selection Framework in Pedestrian Detection. Sensors. 2018;18(7).
MLA
Guo, Zhixin, Wenzhi Liao, Yifan Xiao, et al. “An Occlusion-robust Feature Selection Framework in Pedestrian Detection.” Sensors 18.7 (2018): n. pag. Print.