Real-time human detection using contour cues

Jianxin Wu, Christopher Geyer, James M. Rehg

Research output: Chapter in Book/Report/Conference proceedingConference contribution


A real-time and accurate human detector, C4, is proposed in this paper. C4 achieves 20 fps speed and state-of-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.

Original languageEnglish (US)
Title of host publication2011 IEEE International Conference on Robotics and Automation, ICRA 2011
Number of pages8
StatePublished - 2011
Externally publishedYes
Event2011 IEEE International Conference on Robotics and Automation, ICRA 2011 - Shanghai, China
Duration: May 9 2011May 13 2011

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729


Other2011 IEEE International Conference on Robotics and Automation, ICRA 2011

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering


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