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IROS
2009
IEEE

Detecting pedestrians at very small scales

10 years 5 months ago
Detecting pedestrians at very small scales
— This paper presents a novel image based detection method for pedestrians at very small scales (between 16 x 20 and 32 x 40). We propose a set of new distinctive image features based on collections of local image gradients grouped by a superpixel segmentation. Features are collected and classified using AdaBoost. The positive classified features then vote for potential hypotheses that are collected using a mean shift mode estimation approach. The presented method overcomes the common limitations of a sliding window approach as well as those of standard voting approaches based on interest points. Extensive tests have been produced on a dataset with more than 20000 images showing the potential of this approach.
Luciano Spinello, A. Macho, Rudolph Triebel, Rolan
Added 24 May 2010
Updated 24 May 2010
Type Conference
Year 2009
Where IROS
Authors Luciano Spinello, A. Macho, Rudolph Triebel, Roland Siegwart
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