Human automatic detection and tracking for outdoor video

Abstract

Human automatic tracking is still a problem. This paper presents an approach to treat this problem, where neither the target color model nor the background color model are initially supposed to be known. Besides, the illumination conditions and background may change and, furthermore, the target can be occluded for a determined time. In such scenario, this work proposes an approach for human tracking in outdoor environment by identifying the target. A modified version of Mean Shift is employed, in which it is used HSV color space and a procedure to update its color model. In addition, a method to find the face of the person in video sequence is also presented, in order to identify people or to validate that the target is a human. For evaluation, four metrics were proposed and it has been carried out a series of experiments, and also the presented method was superior to other techniques evaluated.