A tracking algorithm suitable for embedded systems implementation

Particle filters have been widely used for video tracking due to their robustness. However, most particle filter algorithm implementations are computationally expensive which makes them ill-suited for real-time embedded systems. There have been some attempts to provide hardware implementations for the particle filter, but none of them tried to simplify the algorithm first in order to make it more efficient for the hardware implementation. In this paper, a new sampling algorithm inspired from the particle filter methodology is proposed. It includes a resampling scheme that uses a new method to assign the number of particles between filter iterations and a criterion to reduce the number of processed samples, both in order to reduce the computational burden. Our experiments demonstrate that the algorithm can be as accurate as the CONDENSATION algorithm, while reducing the computational load by a factor of 30%.