CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Adaptive Particle Filter for Data Fusion of Multiple Cameras
Journal of VLSI Signal Processing Systems
Multi-camera people tracking by collaborative particle filters and principal axis-based integration
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
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We present a multi modal sequential importance resampling particle filter algorithm for object tracking. We consider a hidden state sequence linked to several observation sequences given by different sensors. In a particle filter based framework, each sensor provides a likelihood (weight) associated to each particle and simple rules are applied to merge the different weights such as addition or product. We propose an original algorithm based on likelihood ratios to merge the observations within the sampling step. The algorithm is compared with classic fusion operations on toy examples. Moreover, we show that the method gives satisfactory results on a real vehicle tracking application.