Visual Data Processing Framework for a Skin-Based Human Detection

Abstract

For a large variety of tasks autonomous robots require a robust visual data processing system. This paper presents a new human detection framework that combines rotation-invariant histogram of oriented gradients (RIHOG) features and binarized normed gradients (BING) pre-processing and skin segmentation. For experimental evaluation a new Human body dataset of over 60000 images was constructed using the Human-Parts dataset, the Simulated disaster victim dataset, and the Servosila Engineer robot dataset. Random, Liner SVM, Quadratic SVM, AdaBoost, and Random Forest approaches were compared using the Human body dataset. Experimental evaluation demonstrated an average precision of 90.4% for the Quadratic SVM model and showed the efficiency of RIHOG features as a descriptor for human detection tasks.