Computer vision and machine learning for enhancing pedestrian safety

Abstract

Accidents involving pedestrians is one of the leading causes of death and injury around the world. Intelligent driver support systems hold a promise to minimize accidents and save many lives. Such a system would detect the pedestrian, predict the possibility of collision, and then warn the driver or engage automatic braking or other safety devices. This chapter describes the framework and issues involved in developing a pedestrian protection system. It is emphasized that the knowledge of the state of the environment, vehicle, and driver are important for enhancing safety. Classification, clustering, and machine learning techniques for effectively detecting pedestrians are discussed, including the application of algorithms such as SVM, Neural Networks, and AdaBoost for the purpose of distinguishing pedestrians from background. Pedestrians unlike vehicles are capable of sharp turns and speed changes, therefore their future paths are difficult to predict. In order to estimate the possibility of collision, a probabilistic framework for pedestrian path prediction is described along with related research. It is noted that sensors in vehicle are not always sufficient to detect all the pedestrians and other obstacles. Interaction with infrastructure based systems as well as systems from other vehicles can provide a wide area situational awareness of the scene. Furthermore, in infrastructure based systems, clustering and learning techniques can be applied to identify typical vehicle and pedestrian paths and to detect anomalies and potentially dangerous situations. In order to effectively integrate information from infrastructure and vehicle sources, the importance of developing and standardizing vehicle-vehicle and vehicle-infrastructure communication systems is also emphasized. © 2008 Springer-Verlag Berlin Heidelberg.