ROBUST HUMAN DETECTION FOR SURVEILLANCE

Abstract

Video Surveillance has received tremendous attention in the present scenario. It has a wide range of applications like it can be used in Border areas of a country or in market areas as well as in the restricted areas for monitoring objects. Human Detection is a field of Video Surveillance where monitoring of humans take place i.e. the human is detected first and its trajectory is estimated for the purpose of monitoring. In this project, a robust human detection method is proposed. The Human Detection System consists of 2 stages. First stage involves Image Pre-processing where the Motion region is extracted and Image Segmentation is applied to this motion region. The second stage classifies the segmented image as a human or a non-human based on Aspect Ratio of Human. So, we can say that the Motion region is incorporated with the Aspect Ratio feature to propose a Robust Human Detection Method. A Dataset is made where the background colour matches with the Human Skin Colour. In this situation it is very difficult to track the human. We propose a system where we can track human under such conditions. The system is tested in PETs Database also and an overall Detection Rate of 85% is reported. However, the Detection rate gets reduced drastically when the human is occluded in the scene.