An effective method for people detection in grayscale image sequences

This paper presents a method for detecting people from images taken with a camera mounted on a robot. The purpose of the detection is avoiding people collision while robot is moving within an unknown environment. It combines two algorithms for this purpose. First, the appearance of people is learned using a set of Haar-like features and the Adaboost algorithm. This information is embedded by building a classifier to differentiate people appearances by other structures. When an image is analyzed for detecting people, regions which contain vertical structures are determined using image gradients. Those regions which have a specific aspect-ratio are selected and the classifier is applied on them. The classifier marks the regions which contain people-like structures. Because this method is desired to be integrated in an autonomous robot navigation system for a dynamic environment, particular attention is paid to increase the speed of the detection as much as possible.