Human Surveillance: A New Non Linear Tracking Technique

This paper presents the theoretical development of nonlinear adaptive filter based on a concept of filtering in high dimensional space (HDS). The most common procedures for nonlinear estimation are the extended Kalman filter. The basic idea of the extended Kalman filter (EKF) is to linearize the state-space model at each time instant around the most recent state estimate. Once a linear model is obtained, the standard Kalman filter equations are applied. Main innovation in this paper is new linearization technique in EKF. The Linearization is performed by converting existing space to high dimensional space. HDS helps having linear space from nonlinear space. In this linear space, the standard Kalman filter gives rise to better results in estimation and prediction purposes. It is proven that MSE and error variance in this space is less than the input space. The proposed EKF is implemented in pedestrian tracking and results show that our method is superior to the standard extended Kalman filter.

Keywords:

Nonlinear adaptive filter , high dimensional space , extended Kalman filter