| 摘要: | 研究期間：10108~10207;In these few decades, the vehicle number is rapidly increasing because people’s incomes are increasing. In addition to the vehicle number, more factors of road situation, driving environment, and human attention result in a large amount of traffic accidents and casualties. If there is a mechanism to help the driver to detect the road situation and driving environment, and then to provide some useful information to the driver in these situations, the danger is therefore avoided. In this research project, we are just combining the techniques of computer vision, image processing, and pattern recognition, and matching the domestic environment to develop state-of-the-art techniques of vision detection to reduce the load of drivers, to reduce the traffic accidents, and then improve the driving safety. From 2001, we have studied the vision detection techniques for the advanced safety vehicle. Based on the versatile computer vision techniques, we proposed methods to detect the situations of driving environment and personal conditions and provide warning to improve the safety of driving. Based on our previous basis and the results, this research project aims various driving conditions to develop new-function and more practical techniques to aid driving. In the previous study, we focus on the development of single-image processing techniques. In this study, we will pay all effort on the processing techniques of sequence and stereo vision images. We propose three studying topics: 1. Motion detection for sequence images: i. multiresolution optical flow estimation for blind spot detection, ii. three low-speed feature-matching-based motion detection, iii. adaptive optical flow estimation for stop-and-go application, iv. motion detection for surrounding top-view detection based on particle filter. 2. Stability analysis for contiguous image sequence: In the vision detection of image squences, there always unstable problem of detection results; such as, the results of i. detection and distance estimation of preceding vehicles, ii. blind-spot detection, iii. guiding lines for parking guiding, iv. pedestrian detection based on the binocular stsreo vision, are always unstable due to the interference of environment, illumination, and other factors. In this study, we will use the extended Kalman filter (EKF) to enhance the motion detection results. 3. Binocular stereo vision detection. Sometime, targets are hard to be detected due to their sizes and appearances. Thus we will use binocular stereo vision method to detect pedestrian and bikes. Binocular stereo vision has the problems of complexity and time consuming. In this study, we will develop the parallel algorithm based on the graphic processing unit (GPU). We also create other three topics in this year, which are ii.3D location and orientation estimation for driver face based on the EKF, iii. Improve the vehicle detection results in night based on EKF, iv. increase the passenger detection rate on a taxi using optical flow information. The studying items in each research topic will be sequentially completed in the following three years. The proposed system seemingly includes too many items; however, most topics are extended form our previous studying results. The key problems of the safety detection techniques are accuracy and stability. The methods concerning the safety vehicles are easily influenced by climate conditions and environment factors; thus the key points of useful methods are whether the proposed methods are not influenced by various climate conditions (sunny, cloudy, rainy, foggy, and night days) ? whether the proposed methods may keep well performance in variant road conditions (freeway, highway, general road, and country roads). Considering the influence factors of climate conditions and environment situations to the vision detection techniques is one characteristic of our development of the computer vision techniques for advanced safety vehicles. The principal investigator of this project is an original researcher on computer vision, he has studied computer vision techniques more than twenty years; moreover, he has the application experience of computer vision aided road vehicle driving for safety more than nine years. From 2008, he has also been a faculty in the Intelligient Mobility Technology Division, Mechanical and Systems Resaerch Lab., ITRI to help the development of the vision detection techniques for vehicles. He has also gotten and applied the US, Taiwan, and China patents in these few years. Partial techniques of the system are practiced and have been employed by several companies; thus we have ability to complete the execution of the research project. |
