Farid Khairallah

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%.

Camera performance considerations for automotive applications

With the cost of image acquisition and processing hardware decreasing substantially, consumer applications utilizing machine vision are becoming more feasible. Automotive vision systems represent one emerging application area and offer the potential of significant enhancements to automotive safety. However, the relative lack of lower-cost and higher-performance cameras limits the use of vision technology in cars. Camera acquisition speed, sensitivity and dynamic range issues are especially critical due to the totally unconstrained illumination for this type of application. A successful vision system must be highly reliable under direct sunlight and near-total darkness. Conditions of extreme contrast occur primarily during the day when deep shadows are cast across part of a scene being imaged by the camera. This paper provides a survey of existing camera hardware and discusses the limitations of existing hardware. Performance criteria requirements for different automotive applications will also be presented.

Camera sensitivity study

As the cost/performance Ratio of vision systems improves with time, new classes of applications become feasible. One such area, automotive applications, is currently being investigated. Applications include occupant detection, collision avoidance and lane tracking. Interest in occupant detection has been spurred by federal automotive safety rules in response to injuries and fatalities caused by deployment of occupant-side air bags. In principle, a vision system could control airbag deployment to prevent this type of mishap. Employing vision technology here, however, presents a variety of challenges, which include controlling costs, inability to control illumination, developing and training a reliable classification system and loss of performance due to production variations due to manufacturing tolerances and customer options. This paper describes the measures that have been developed to evaluate the sensitivity of an occupant detection system to these types of variations. Two procedures are described for evaluating how sensitive the classifier is to camera variations. The first procedure is based on classification accuracy while the second evaluates feature differences.