IEEE Transactions on Pattern Analysis and Machine Intelligence
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Particle filters for positioning, navigation, and tracking
IEEE Transactions on Signal Processing
Detection and classification of highway lanes using vehicle motion trajectories
IEEE Transactions on Intelligent Transportation Systems
A Generic Framework for Tracking Using Particle Filter With Dynamic Shape Prior
IEEE Transactions on Image Processing
Integrating the projective transform with particle filtering for visual tracking
Journal on Image and Video Processing - Special issue on advanced video-based surveillance
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This article introduces a new particle filtering approach for object tracking in video sequences. The projective particle filter uses a linear fractional transformation, which projects the trajectory of an object from the real world onto the camera plane, thus providing a better estimate of the object position. In the proposed particle filter, samples are drawn from an importance density integrating the linear fractional transformation. This provides a better coverage of the feature space and yields a finer estimate of the posterior density. Experiments conducted on traffic video surveillance sequences show that the variance of the estimated trajectory is reduced, resulting in more robust tracking.