Abstract
Co-occurrence histograms of oriented gradients (CoHOG) is a powerful feature descriptor for pedestrian detection, but its calculation cost is large because the feature vector is very high-dimensional. In this paper, in order to achieve rapid detection, we propose a novel method to divide the CoHOG feature into small features and construct a cascade-structured classifier by combining many weak classifiers. The proposed cascade classifier rejects non-pedestrian images at the early stage of the classification while positive and suspicious images are examined carefully by all weak classifiers. This accelerates the classification process without spoiling detection accuracy. The experimental results show that our method achieves about 2.6 times faster detection and the same detection accuracy in comparison to the previous work.
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Hiromoto, M., Miyamoto, R. (2009). Cascade Classifier Using Divided CoHOG Features for Rapid Pedestrian Detection. In: Fritz, M., Schiele, B., Piater, J.H. (eds) Computer Vision Systems. ICVS 2009. Lecture Notes in Computer Science, vol 5815. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04667-4_6
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DOI: https://doi.org/10.1007/978-3-642-04667-4_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04666-7
Online ISBN: 978-3-642-04667-4
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