Tracking of Multiple Moving Objects in Dynamic Image of Omni-Directional Camera Using PHD Filter

Abstract

A method of multiple moving objects tracking in dynamic image of omni-directional camera has been proposed. Finite random set (FRS) based state space model is employed in the method due to its inherent nature capable to represent the scene having occlusion and appearance of object as well as missing and false detection in observation. Sequential Monte Carlo (SMC) implementation of Probability hypothesis density (PHD) filter has been used for estimating state of the state space model. The state is a finite random set of single object states, where each element of the set consists of position and velocity of the object in panoramic image coordinate of omni-directional camera image. We propose a new method to display tracking result from weighted particles obtained from the estimation process by SMC implementation of PHD filter. Key idea of the method is to put an integer label on each particle, where the label indicates specific object among multiple objects in the image scene tracked by the particle. Numerical simulation and real image experiments illustrate tracking performance of the proposed method.