HOG based Fast Human Detection

TL;DR

This paper introduces a real-time human detection algorithm using HOG features and SVM classification, suitable for robotics applications, demonstrating effective performance in practical scenarios.

Contribution

The paper presents a novel real-time human detection method based on HOG features and SVM, optimized for mobile robot environments.

Findings

Effective real-time detection in robotics scenarios

High accuracy with HOG and SVM approach

Suitable for integration in mobile robot systems

Abstract

Objects recognition in image is one of the most difficult problems in computer vision. It is also an important step for the implementation of several existing applications that require high-level image interpretation. Therefore, there is a growing interest in this research area during the last years. In this paper, we present an algorithm for human detection and recognition in real-time, from images taken by a CCD camera mounted on a car-like mobile robot. The proposed technique is based on Histograms of Oriented Gradient (HOG) and SVM classifier. The implementation of our detector has provided good results, and can be used in robotics tasks.