Human Detection for Video Surveillance in Hospital

This paper presents a human detection system in a vision-based hospital surveillance environment. The system is composed of three subsystems, i.e. background segmentation subsystem (BSS), human feature extraction subsystem (HFES), and human recognition subsystem (HRS). The codebook background model is applied in the BSS, the histogram of oriented gradients (HOG) features are used in the HFES, and the support vector machine (SVM) classification is employed in the HRS. By means of the integration of these subsystems, the human detection in a vision-based hospital surveillance environment is performed. Experimental results show that the proposed system can effectively detect most of the people in hospital surveillance video sequences.