Object tracking from image sequences using adaptive models in fuzzy particle filter

摘要

This paper describes a vision-based system for tracking objects from image sequences. The proposed system has the standard architecture with a particle filter which is a popular algorithm to track objects in real time. Many tracking algorithms have a great difficulty in tracking objects robustly by reason of complex background and rapid changes under a real complex environment such as a traffic road. To make a robust algorithm for object tracking, we propose the method that uses the adaptive autoregressive model as a state transition model and the adaptive appearance mixture model as an observation model. But, in case of changing the state of a tracked object suddenly, the adaptive models may not make the optimal parameters for accurate states at current time. Because the noise variance of the adaptive models in this case is larger than that in normal case, it has an effect on the accuracy of an object tracking algorithm. Thus, we propose a fuzzy particle filter to overcome problems from the occurrence of the unexpected improper variances due to several causes. In this paper, as the process noises and the observation noises in a fuzzy particle filter are considered as fuzzy variables by using the possibility theory, a fuzzy particle filter with fuzzy noises is used to manage uncertainty in various noise models. Also, we make possibility measure as using the fuzzy relation equation which is defined by these fuzzy variables. And then, the states are estimated by using a fuzzy expected value operator. Also, because the proposed algorithm applies several functions to improve the accuracy of tracking an object, the performance of tracking speed deteriorates. To resolve this problem to some extent, we consider the fact that a fuzzy particle filter has a little bit of an effect on the number of particles. Consequently, we propose the method which can adjust the number of particles by using the result from a measurement step in order to improve the speed for an object tracking in the proposed algorithm. The experiments of this paper show that the proposed method is efficient and has many advantages for an object tracking in real environments.