Learning and inferring transportation routines
Artificial Intelligence
Partial Linear Gaussian Models for Tracking in Image Sequences Using Sequential Monte Carlo Methods
International Journal of Computer Vision
Tracking Deforming Objects Using Particle Filtering for Geometric Active Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Tracking Closed Curves with Non-linear Stochastic Filters
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
Cyclone tracking using multiple satellite image sources
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Data-driven trajectory smoothing
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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The paper presents an extension of the particle filtering algorithm that is applicable when an accurate state prediction model cannot be specified but a database of prior state evolution tracks is available. The conventional particle filtering algorithm represents the belief state as a collection of particles, where each particle is a sample from the state space. The particles are updated by applying the state space equations. In the proposed approach, each particle is an instance of a complete state trajectory, drawn from the database of historic state trajectories. An explicit state update model is not required as the trajectory represented by each particle is covers the entire modeling time period. When new observations become available, a proportion of the particles are replaced using trajectories from the database, selected based on distance from the observation. This tracking algorithm is applicable where the state evolves in a complex manner as in the eye of tropical cyclones. The proposed technique is evaluated by tracking selected cyclones from 2005 using a database of cyclone tracks from the previous 25 years.