A real-time system for monitoring of cyclists and pedestrians

Abstract

Camera based fixed systems are routinely used for monitoring highway traffic. For this purpose inductive loops and microwave sensors are mainly used. Both techniques achieve very good counting accuracy and are capable of discriminating trucks and cars. However pedestrians and cyclists are mostly counted manually. In this paper, we describe a new camera based automatic system that utilizes Kalman filtering in tracking and Learning Vector Quantization (LVQ) for classifying the observations to pedestrians and cyclists. Both the requirements for such systems and the algorithms used are described. The tests performed show that the system achieves around 80%-90% accuracy in counting and classification.