Particle filters are a class of sequential Monte-Carlo methods which are used commonly when estimating various unknowns of the time-varying signals presented in real time, especially when dealing with nonlinearity and non-Gaussianity in BOT applications. This thesis work is designed to perform one such estimate involving tracking a person using the road information available from an IR surveillance video. In this thesis, a parallel custom hardware is implemented in Altera cyclone IV E FPGA device utilizing SIRF type of particle filter. This implementation has accounted how the algorithmic aspects of this sampling based filter relate to possibilities and constraints in a hardware implementation. Using 100MHz clock frequency, the synthesised hardware design can process almost 50 Mparticles/s. Thus, this implementation has resulted in tracking the target, which is defined by a 5-dimensional state variable, using the noisy measurements available from the sensor.