Human Detection Based on HOG-LBP for Monitoring Sterile Zone

Human detection has enormous application area in autonomous video surveillance and human-computer interaction. Detecting suspicious event has become a very crucial issue in the current circumstance of our society. As a pioneer, a framework is proposed for detecting human in the sterile zone in this paper. Since, in the case of the sterile zone, we have to deal with low-resolution video, that's why initially input video frames are enhanced by using local histogram equalization. Then a background model is created by using the Gaussian Mixture Model (GMM) where each pixel is represented by a mixture of a number of Gaussian based on probabilistic method. This modeled background is then compared with a new frame to detect the foreground object. After that, the morphological operation is performed to remove discontinuities and to get the region of interest (ROI). Then shape and texture features from ROI are extracted for classification. Finally, combined features from Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) are fed into SVM classifier to detect human. In this paper to achieve better performance in the sterile zone, human shape is analyzed with HOG along with enumerating local features by LBP. Moreover, this proposed framework is tested using various video in different conditions and the outcome demonstrates remarkable efficiency comparative to other alternatives.