Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
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ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
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VS '98 Proceedings of the 1998 IEEE Workshop on Visual Surveillance
Real Time Face and Object Tracking as a Component of a Perceptual User Interface
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Mobile Robot Localization Method Based on Adaptive Particle Filter
ICIRA '08 Proceedings of the First International Conference on Intelligent Robotics and Applications: Part I
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IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Tracking a moving object via a sensor network with a partial information broadcasting scheme
Information Sciences: an International Journal
Efficient visual tracking using particle filter with incremental likelihood calculation
Information Sciences: an International Journal
Filtering of colored noise for speech enhancement and coding
IEEE Transactions on Signal Processing
Recursive autoregressive spectral estimation by minimization of thefree energy
IEEE Transactions on Signal Processing
Expected value of fuzzy variable and fuzzy expected value models
IEEE Transactions on Fuzzy Systems
Fuzzy Particle Filtering for Uncertain Systems
IEEE Transactions on Fuzzy Systems
Visual tracking and recognition using appearance-adaptive models in particle filters
IEEE Transactions on Image Processing
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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.