  Pedestrian detection is a research hotspot and a difficult issue in the
computer vision such as the Intelligent Surveillance System, the Intelligent
Transport System, robotics, and automotive safety. However, the human body's
position, angle, and dress in a video scene are complicated and changeable,
which have a great influence on the detection accuracy. In this paper, through
the analysis on the pros and cons of Census Transform Histogram (CENTRIST), a
novel feature is presented for human detection Ternary CENTRIST (T-CENTRIST).
The T-CENTRIST feature takes the relationship between each pixel and its
neighborhood pixels into account. Meanwhile, it also considers the relevancy
among these neighborhood pixels. Therefore, the proposed feature description
method can reflect the silhouette of pedestrian more adequately and accurately
than that of CENTRIST. Second, we propose a fast pedestrian detection framework
based on T-CENTRIST in infrared image, which introduces the idea of extended
blocks and the integral image. Finally, experimental results verify the
effectiveness of the proposed pedestrian detection method.

Computer Vision and Pattern Recognition