Mono-Camera based 3D Object Tracking Strategy for Autonomous Vehicles

This paper proposes an approach to calculate 3D positions of far detected vehicles. Mainly, the distance from the vehicles during autonomous driving must be estimated precisely to strategize a safe path planning. A 3D camera model is created to map the pixel positions to the distance values with respect to the vehicle plane and the distortion parameters. In order to refine the distance accuracy, the Extended Kalman Filter (EKF) framework is designed to track the detected vehicles based on the derivative relationship between the camera and world coordinate systems. The experimental results indicate that the proposed method is capable to successfully track 3D positions with sufficient accuracy compared to LIDAR and Radar based tracking systems in terms of cost and stability.