Author : Ashwani Kumar and Atiika Aggarwal

Tracking of a dynamic object is a challenging task and it becomes more tedious when multiple dynamic objects are present in the targeted zone. The main task in object tracking is to filter the movement information from undesired dynamic objects because this information is considered as noise. To overcome this problem, the implementation of kalman filter is presented which is used to track the desired dynamic object and to filter the noise in 2D object tracking by the estimation of past, present and future states of object. The estimation of current state depends on the variables i.e. time, velocity, covariance and noise mainly. The hardware implementation of kalman filter is done on FPGA (Virtex 5) for parallel and pipelined architectures for optimum recursive data processing (ORDP) algorithm, using Verilog HDL on Xilinx ISE simulator in the range of MHz clock frequency by keeping the focus on reducing the hardware area and increasing the speed of operation.

Keywords: Kalman Filter, FPGA, Object Tracking, Prediction Model, Measurement Model, Verilog HDL

Article published in International Journal of Current  Engineering  and Technology, Vol.4,No.3 (June- 2014)