Particle filters are advanced for building robust object trackers capable of operation under difficult tracking conditions. An Excitation Particle Filter (EPF) is introduced in this book for object tracking. A new likelihood model is proposed. It depends on multiple likelihood functions: position likelihood; gray level intensity likelihood and similarity likelihood. Also, we modify the PF as a robust estimator to overcome the well-known sample impoverishment problem of the PF. The proposed enhanced PF (EPF) is implemented in software and evaluated. Simulation results demonstrated the superior performance of the proposed tracker in terms of accuracy, robustness and occlusion over classical tracking algorithms. Three efficient novel hardware architectures of the Sample Important Resample Filter (SIRF) and the EPF are introduced and implemented on FPGA platform. These architectures feature speed improvement, efficient memory utilization, and/or hardware resource saving.