Vehicle tracking using fractional order Kalman filter for non-linear system

Road intersections are more prone to traffic congestion, which leads to traffic accidents. It is important to monitor the traffic congestion at crossings for regulating the driver behaviour and preventing the accidents. As real time tracking systems rely on the accuracy of the system, an approach has been proposed for vehicle tracking. This paper describes a real time tracking approach for non-linear systems. The occluded vehicle is extracted from the image sequences by subtracting the image from the modelled background. Vehicles are tracked using modified fractional order Kalman filter with better accuracy. The non-linearity of the system is linearised using Jacobian. The impact of behaviour of vehicle on error covariance has been reduced using modified transition matrix. The fractional states are calculated using GL fractional derivative definition. The proposed method is tested for various motion models and is evaluated using root mean square error with different data sets. It has been shown that the root mean square error has reduced using the proposed method.