Stochastic filters for object tracking

Stochastic filters have been extensively used for object tracking because of its ability to measure uncertainties and high accuracy. In recent years, the availability of cheap computers with high computational power has led to incorporate tracking systems in many consumer electronics devices such as surveillance cameras and game consoles. In this paper, we compare Kalman filter and particle filter tracking based on their computational time and estimation accuracy. These two filters represent 50% of the published work on object tracking in the last five years.