Human head detection using multi-modal object features

This paper describes a neural network system that automatically detects whether a human head exists in a given image. We focus our research in the first two levels of head detections. At the first level, it extracts candidates of a head using range information, motion clue and 3D spherical shape. At the second level, the system uses multiple visual modalities including gray scale value distribution, shape, motion and range information obtained using a stereo vision system to represent head features. A neural network classifier is used to evaluate the effectiveness of various object features for generating and representing human head. The system is validated on a large collection of images taken from a stereo camera system mounted inside a vehicle. Our experiments show the presented system has an accurate rate over 96%.