  Surveillance is essential for the safety of power substation. The detection
of whether wearing safety helmets or not for perambulatory workers is the key
component of overall intelligent surveillance system in power substation. In
this paper, a novel and practical safety helmet detection framework based on
computer vision, machine learning and image processing is proposed. In order to
ascertain motion objects in power substation, the ViBe background modelling
algorithm is employed. Moreover, based on the result of motion objects
segmentation, real-time human classification framework C4 is applied to locate
pedestrian in power substation accurately and quickly. Finally, according to
the result of pedestrian detection, the safety helmet wearing detection is
implemented using the head location, the color space transformation and the
color feature discrimination. Extensive compelling experimental results in
power substation illustrate the efficiency and effectiveness of the proposed
framework.
