Estimation of Object Motion Parameters from Noisy Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
How Does CONDENSATION Behave with a Finite Number of Samples?
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Mixed-State Condensation Tracker with Automatic Model-Switching
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Articulated Body Motion Capture by Stochastic Search
International Journal of Computer Vision
A particle filter for joint detection and tracking of color objects
Image and Vision Computing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
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Object tracking is a challenging problem due to the presence of noise, occlusion, clutter and dynamic change in the scene other than the motion of the object of interest. A variety of tracking algorithms has been proposed and implemented to overcome the related difficulties, but there are still some problems need to be covered. In this paper, we present an approach for multiple objects tracking based on particle filter algorithm. We use the particle filter to predict the trajectory of the target. The problem of occlusion is predicted based on the likelihood measurement and estimated samples distance. The particle filter approximates a posterior probability density of the state using samples or particles. Each state is denoted as the hypothetical state of the tracked object and its weight which is predicted based on the system model. In this paper, the state is treated as a position, speed, size, scale and appearance of the object. The samples weight is considered as the likelihood of each particle which is measured based on the similarity between the colour feature of the target model and the objects. And finally, the mean state of the particles is treated as the estimated state of the object. The experiments are performed to confirm the effectiveness of the method to track multiple objects.