Sequential importance sampling filtering for target tracking in image sequences

We propose in this letter a new approach to direct target tracking in cluttered image sequences using sequential importance sampling (SIS). We use Gauss-Markov random field modeling to describe the clutter correlation and incorporate the clutter and target signature models into the design of the SIS tracking algorithm. We quantify the performance of the SIS tracker using a simulated image sequence generated from real infrared airborne radar data and compare it to the performance of a grid-based hidden Markov model tracker. Simulation results show good performance for the proposed algorithms in a scenario of very low target-to-clutter ratio.