Interacting multiple model (IMM) Kalman filters for robust high speed human motion tracking

Abstract

Accurate and robust tracking of humans is of growing interest in the image processing and computer vision communities. The ability of a vision system to track the subjects and accurately, predict their future locations is critical to many surveillance and camera control applications. Further, an inference of the type of motion as well as to rapidly detect and switch between motion models is critical since in some applications the switching time between motion models can be extremely small. The interacting multiple model (IMM) Kalman filter provides a powerful framework for performing the tracking of both the motion as well as the shape of these subjects. The tracking system utilizes a simple geometric shape primitive such as an ellipse to define a bounding extent of the subject. The utility of the IMM paradigm for rapid model switching and behaviour detection is shown for a passenger airbag suppression system in an automobile. The simplicity, of the methods and the robustness of the underlying IMM filtering make the framework well suited for low-cost embedded real-time motion sequence analysis systems.