Filtered channel features for pedestrian detection

This paper starts from the observation that multiple top performing pedestrian detectors can be modelled by using an intermediate layer filtering low-level features in combination with a boosted decision forest. Based on this observation we propose a unifying framework and experimentally explore dif...

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Published in:2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 1751 - 1760
Main Authors: Zhang, Shanshan, Benenson, Rodrigo, Schiele, Bernt
Format: Conference Proceeding Journal Article
Language:English
Published: IEEE 01.06.2015
Subjects:
Channels
Computer vision
Conferences
Decision trees
Detectors
Feature extraction
Filter banks
Filtering
Image color analysis
Optical filters
Optical flow
Pattern recognition
Pedestrians
Recall
Shape
Training
ISSN:1063-6919, 1063-6919
Online Access:Get full text
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Abstract This paper starts from the observation that multiple top performing pedestrian detectors can be modelled by using an intermediate layer filtering low-level features in combination with a boosted decision forest. Based on this observation we propose a unifying framework and experimentally explore different filter families. We report extensive results enabling a systematic analysis. Using filtered channel features we obtain top performance on the challenging Caltech and KITTI datasets, while using only HOG+LUV as low-level features. When adding optical flow features we further improve detection quality and report the best known results on the Caltech dataset, reaching 93% recall at 1 FPPI.
AbstractList This paper starts from the observation that multiple top performing pedestrian detectors can be modelled by using an intermediate layer filtering low-level features in combination with a boosted decision forest. Based on this observation we propose a unifying framework and experimentally explore different filter families. We report extensive results enabling a systematic analysis. Using filtered channel features we obtain top performance on the challenging Caltech and KITTI datasets, while using only HOG+LUV as low-level features. When adding optical flow features we further improve detection quality and report the best known results on the Caltech dataset, reaching 93% recall at 1 FPPI.
Author Zhang, Shanshan
Benenson, Rodrigo
Schiele, Bernt
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  givenname: Rodrigo
  surname: Benenson
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  organization: Max Planck Institute for Informatics, Saarbrücken, Germany
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  givenname: Bernt
  surname: Schiele
  fullname: Schiele, Bernt
  organization: Max Planck Institute for Informatics, Saarbrücken, Germany
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SubjectTerms Channels
Computer vision
Conferences
Decision trees
Detectors
Feature extraction
Filter banks
Filtering
Image color analysis
Optical filters
Optical flow
Pattern recognition
Pedestrians
Recall
Shape
Training
Title Filtered channel features for pedestrian detection
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