Performance evaluation of local features in human classification and detection

  • Paisitkriangkrai S
  • Shen C
  • Zhang J
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Abstract

Detecting pedestrians accurately is the first fundamental step for
many computer vision applications such as video surveillance, smart
vehicles, intersection traffic analysis and so on. The authors present
an experimental study on pedestrian detection using state-of-the-art
local feature extraction and support vector machine (SVM) classifiers.
The performance of pedestrian detection using region covariance,
histogram of oriented gradients (HOG) and local receptive fields
(LRF) feature descriptors is experimentally evaluated. The experiments
are performed on the DaimlerChrysler benchmarking data set, the MIT
CBCL data set and 'Intitut National de Recherche en Informatique
et Automatique (INRIA) data set. All can be publicly accessed. The
experimental results show that region covariance features with radial
basis function kernel SVM and HOG features with quadratic kernel
SVM outperform the combination of LRF features with quadratic kernel
SVM. Furthermore, the results reveal that both covariance and HOG
features perform very well in the context of pedestrian detection.

Author-supplied keywords

  • DaimlerChrysler benchmarking data set;Intitut Nati

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Authors

  • S Paisitkriangkrai

  • C Shen

  • J Zhang

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