Position estimation and multiple obstacles tracking method based on stereo vision system

In this paper, we present a method to estimate obstacles´ position and track multiple obstacles on the road based on a stereo vision system. A stereo vision system can measure distance to an obstacle using disparity. However, this system has several problems such as sampling error, geometric problems due to the installation of a stereo camera, and image distortion in the calibration and rectification processes that cause deterioration in accuracy and reliability. We utilize a multi-layer perceptron (MLP) method to correct mean error, and also a strong tracking interacting multiple model (ST-IMM) Kalman filter is proposed to minimize the error variance. The ST-IMM has robustness for maneuver and non-stationary error variance. ST-IMM has an advantage that one model can complement another model´s shortcomings by using several sub-models. A simple data association method based on nearest neighborhood filtering is proposed to track multiple obstacles. The experiment results show that our algorithms can estimate the target´s position and track multiple objects within about 4% distance error in range of 10 to 50 m, even when the target vehicle maneuvers rapidly.