Multi-Object tracking based on Kalman Filtering Combining Radar and Image Measurements

Abstract

The purpose of this paper is to develop a tracking system. The designed platform is based on two kinds of physical sensors: a doppler radar module and HD Camera. All the measurements from those modules are processed using a data fusion method. In this work, a method based on the Gaussian mixture model is used for foreground detection (i.e. background subtraction). After that our move to a filtering step which is used to refine the detection results firstly obtained, then a tracking process is introduced. As a final stage, all the vision-based measurements are combined with the processed radar raw data. Here, the goal is to perfectly estimate the target velocity in the real time. Added to target 2D positions, this speed information is considered as a third dimension. This is very useful in many applications such as traffic control, robotics, autonomous vehicles etc. In this work a set of experiments is conducted in order to validate the developed tracking method.