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LBP Channels for Pedestrian Detection


This paper introduces a new channel descriptor for pedestrian detection. This type of descriptor usually selects a set of one-valued filters within the enormous set of all possible filters for improved efficiency. The main claim underpinning this paper is that the recent works on channel-based features restrict the filter space search, therefore bringing along the obsolescence of one-valued filter representation. To prove our claim, we introduce a 12-valued filter representation based on local binary patterns. Indeed, various improvements now allow for this texture feature to provide a very discriminative, yet compact descriptor. Filter selection boasting new combination restrictions as well as a reverse selection process are also presented to choose the best filters. experiments on the INRIA and Caltech-USA datasets validate the approach.
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Dates and versions

hal-01849431 , version 1 (26-07-2018)


  • HAL Id : hal-01849431 , version 1


Remi Trichet, Francois Bremond. LBP Channels for Pedestrian Detection. WACV , Mar 2018, Lake Tahoe, United States. ⟨hal-01849431⟩
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