We describe a novel method whereby a particle filter is used to create a potential field for robot control without prior clustering. We show an application of this technique to control a team of mobile robots to cooperatively locate and track a moving target. The particle filter models a probability distribution over the estimated location of the target, providing robust tracking despite freque...

This paper deals with the problem of maneuvering target tracking in wireless tracking service. It results in a mixed linear/non-linear Models estimation problem. For maneuvering tracking systems, these problems are traditionally handled using the extended Kalman filter or Particle filter. In this paper, Marginalized Particle Filter is presented for applications in such problem. The algorithm ma...

Tracking an object's 3D position and orientation from a color image can been accomplished with particle filters if its color and shape properties are known. Unfortunately, initialization in particle filters is often manual or random, thus rendering the tracking recovery process slow or no longer autonomous. A method that uses image data to generate likely pose hypotheses for known objects is pr...

Particle-tracking methods are widely used in fluid mechanics and multi-target tracking research because of their unique ability to reconstruct long trajectories with high spatial and temporal resolution. Researchers have recently demonstrated 3D tracking of several objects in real time, but as the number of objects is increased, real-time tracking becomes impossible due to data transfer and pro...

Object tracking through multiple cameras is a popular research topic in security and surveillance systems especially when human objects are the target. However, occlusion is one of the challenging problems for the tracking process. This paper proposes a multiple-camera-based cooperative tracking method to overcome the occlusion problem.  The paper presents a new model for combining convolutiona...

A particle tracking code is developed to solve a general time-fractional diffusion equation (FDE), yielding a Lagrangian framework that can track particle dynamics. Extensive simulations demonstrate the efficiency and flexibility of this simple Langevin approach. Many real problems require a vector FDE with variable parameters and multiscaling spreading rates. For these problems, particle track...