Learning-based human detection using difference image in visual surveillance
Jong-Eun Ha, Dong‐Joong Kang, Wangheon Lee · 2010
In visual surveillance, accurate detection of each human is important for various application of counting and tracking of people. Applying general human detection algorithm for each image could be applied. In this paper, we propose a learning-based human segmentation algorithm. Histogram of Oriented Gradient (HOG) shows remarkable result on human detection and it uses intensity image. We use difference image as a training sample and it is obtained through the accumulation of multiple difference images. We show that proposed algorithm could be a good candidate for the fast generation of possible regions of human in visual surveillance. We show the feasibility of proposed algorithm using publicly available data sets.