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In this paper, we present an event-driven track management method to detect reliably and track robustly while minimizing missing and false detections. No state-of-the-art vehicle detection method can detect all the vehicles on the road without error. A multi-vehicle tracking method is essential to minimize the number of missing and false detections. In a multi-vehicle tracking method, there are three types of errors: false negative alarms, false positive alarms, and track identity switches. Our track management method can reduce the number of these errors remarkably while processing in real time for online application. Our track management method has four states: IDLE, PRE-TRACK, CUR-TRACK, and POST-TRACK. Most false positive alarms are removed in the PRE-TRACK state due to their sparseness. A track state transition to other states is determined by a track score. The track score is calculated by obstacle detection, vehicle recognition, detection-by-tracking, and data association. The proposed method is tested and verified with image sequences in real road environments. The experimental results demonstrate that the event-driven track management method minimizes the number of the false positive and false negative alarms remarkably compared with previous methods.978-1-4577-0891-6/11/$26.00 ©2011 IEEE

We present a distance measurement method based on stereo vision system while guaranteeing accuracy and reliability. It has been considered as difficult problem to measure both long and short distance with a stereo vision system accurately due to sampling error and camera sensor error. To resolve these problems of the stereo vision system, we utilize an algorithm which is consisted of a modified sub-pixel displacement method to enhance the accuracy of disparity and Strong Tracking Kalman filter (STKF) to reduce the camera sensor errors. Our displacement method and the usefulness of STKF are verified as compared to other displacement methods and Conventional Kalman filter (CKF) through simulating on the several distance ranges. The Monte-Carlo simulation results show that our algorithm is capable of measuring up to hundreds of meters while root mean square error (RMSE) maintains about 0.04 at all ranges, even though the target vehicle maneuvers or moves nonlinearly.