Integral and Indexing based Feature Extraction Method for Human Detection and Tracking

Abstract

The detection and tracking of the human being in Video surveillance is a vibrant research topic in computer vision. It replaces old traditional method of monitoring camera feed by the human being by automatically detect and tracking along with the understanding of human behavior. Surveillance systems must be self-directed to improve the performance and eliminate such operator errors. Ideally, an automated surveillance system should only require the objectives of an application, in which there are real-time understanding of activities. Human detection includes detecting humans in each frame of video. Either each tracking method requires an object detection mechanism in every frame or when the object first appears in the video. Human tracking is the method of detecting human over time on the camera feed. The fast and efficient camera, high-quality videos, low priced camera and increasing need for the automatic system for detection and tracking will create interest in human tracking algorithms.