Real Time Detection and Tracking of a Model Car using Kalman Filter

This paper elucidates a procedure for real time detection and tracking of a moving model car in the sequence of image frames acquired by a static camera. The vehicle tracking is performed by exploiting the modelling behavior of Kalman filter. The raw image acquisition, background subtraction, detection of vehicle in image frames is performed using image processing techniques. The vehicle tracking is performed by employing the modelling efficiency of a traditional Kalman filter. The raw image acquisition, background subtraction, to detect vehicle in image frames is performed using image processing techniques. The vehicle parameter estimation is further performed using the Kalman filter. This system takes raw image of two dimensional (2D) drivable surface with car as input, and outputs the spatial coordinates of estimated car position. The system was implemented on a ‘VEEROBOT’ robotic car in the laboratory environment. Real time images were taken using a ‘Logitech’ webcam, and the relevant processing is carried out using ‘Raspberry pi 3’. The analysis of experimental results between the measured and estimated position of the car is presented in this paper.