HUMAN BODY SEGMENTATION IN A STATIC IMAGE WITH ON-LINE ADABOOST AT MULTISCALE SUPERPIXELS

Abstract

In this work, we propose a new method for accurate human body detection in a static image with multi-scale superpixels based on two models. First, based on the face detection, we use designed torso part model to estimate the torso part region to provide the positive samples of on-line AdaBoost. Then, according to segmented torso, we estimate the hip region using the designed torso model to provide the positive samples of on-line AdaBoost. Finally, we combine the segmented part and the skin region for body. The main contributions of this work are as follows: (1) A new framework for human body detection using multi-scale superpixels and classifier with auto-threshold is proposed. (2) Two models have been designed for the human body segmentation. Experimental results show that the framework can accurately detect the human body effectively and efficiently.