Title:
Real-Time Human Detection Using Contour Cues
Real-Time Human Detection Using Contour Cues
Author(s)
Wu, Jianxin
Geyer, Christopher
Rehg, James M.
Geyer, Christopher
Rehg, James M.
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Abstract
A real-time and accurate human detector, C⁴, is
proposed in this paper. C⁴ achieves 20 fps speed and stateof-
the-art detection accuracy, using only one processing thread
without resorting to special hardwares like GPU. Real-time accurate
human detection is made possible by two contributions.
First, we show that contour is exactly what we should capture
and signs of comparisons among neighboring pixels are the
key information to capture contours. Second, we show that
the CENTRIST visual descriptor is particularly suitable for
human detection, because it encodes the sign information and
can implicitly represent the global contour. When CENTRIST
and linear classifier are used, we propose a computational
method that does not need to explicitly generate feature
vectors. It involves no image pre-processing or feature vector
normalization, and only requires O(1) steps to test an image
patch. C⁴ is also friendly to further hardware acceleration. In a
robot with embedded 1.2GHz CPU, we also achieved accurate
and 20 fps high speed human detection.
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Date Issued
2011-05
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Text
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Proceedings