Browsing by Author "Lin, Chuan-Tsai"

Now showing 1 - 5 of 5

Dynamic Calibration and Occlusion Handling Algorithms for Lane Tracking
(2009-05-01)
Wu, Bing-Fei
;
Lin, Chuan-Tsai
;
Chen, Yen-Lin
;
電控工程研究所
;
Institute of Electrical and Control Engineering
An approach of rapidly computing the projective width of lanes is presented to predict the projective positions and widths of lanes. The Lane Marking Extraction Finite State Machine is designed to extract points with features of lane markings in the image, and a cubic B-spline is adopted to conduct curve fitting to reconstruct road geometry. A statistical search algorithm is also proposed to correctly and adaptively determine thresholds under various kinds of illumination conditions. Furthermore, the parameters of the camera in a moving car may change with (be vibration, so a dynamic calibration algorithm is applied to calibrate camera parameters and lane widths with the information of lane projection. Moreover, a fuzzy logic is applied to determine the situation of occlusion. Finally, a region-of-interest determination strategy is developed to reduce the search region and to make the detection more robust with respect to the occlusion on the lane markings or complicated changes of curves and road boundaries.
Range and Size Estimation Based on a Coordinate Transformation Model for Driving Assistance Systems
(2009-09-01)
Wu, Bing-Fei
;
Lin, Chuan-Tsai
;
Chen, Yen-Lin
;
交大名義發表
;
National Chiao Tung University
This paper presents new approaches for the estimation of range between the preceding vehicle and the experimental vehicle, estimation of vehicle size and its projective size, and dynamic camera calibration. First, our proposed approaches adopt a camera model to transform coordinates from the ground plane onto the image plane to estimate the relative position between the detected vehicle and the camera. Then, to estimate the actual and projective size of the preceding vehicle, we propose a new estimation method. This method can estimate the range from a preceding vehicle to the camera based on contact points between its tires and the ground and then estimate the actual size of the vehicle according to the positions of its vertexes in the image. Because the projective size of a vehicle varies with respect to its distance to the camera, we also present a simple and rapid method of estimating a vehicle's projective height, which allows a reduction in computational time for size estimation in real-time systems. Errors caused by the application of different camera parameters are also estimated and analyzed in this study. The estimation results are used to determine suitable parameters during camera installation to suppress estimation errors. Finally, to guarantee robustness of the detection system, a new efficient approach to dynamic calibration is presented to obtain accurate camera parameters, even when they are changed by camera vibration owing to on-road driving. Experimental results demonstrate that our approaches can provide accurate and robust estimation results of range and size of target vehicles.
Robust image measurement and analysis based on perspective transformations
(2006)
Wu, Bing-Fei
;
Lin, Chuan-Tsai
;
電控工程研究所
;
Institute of Electrical and Control Engineering
In the paper, perspective transformation is used to project points from the front horizon of the camera to an image plane and thus to measure the distance between the detected object and the camera. With the information about points on the ground, the projective positions of every tip in a rigid object can be figured out through transformation. Features of the object's projection at different distances, such as size and shape, can also be predicted. Besides, the paper has analyzed difference in the result of the measurement and errors caused by the application of different parameters. The information assists engineers of vision-based detection system in determining the parameters of the system and identifying features of an object's projection to accelerate the detection. Also, the camera parameters compensate automatically when being influenced by outer force to promote the effects of detection and make a robust system.
Robust lane detection and tracking for driving assistance systems
(2007)
Wu, Bing-Fei
;
Lin, Chuan-Tsai
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This paper describes a novel approach of detecting lanes. The projective width of lanes can be computed precisely by a method of geometric projection to predict the relative position and features of lane markings. Besides, a camera's accurate tilt angle and the lane width can be acquired by dynamic calibration. Also, an algorithm named Lane Marking Extraction (LME) Finite State Machine (FSM) is proposed to capture the points with features of lane markings and use fuzzy reasoning to discriminate points of the lane markings. Some points are chosen as knots to supplement all of the points on the lane boundary with cubic B-spline interpolation, which reconstructs the area of lanes with a left and a right curve. The proposed approach uses prediction of a lane's projective width and the way of tracking to accelerate lane detections and to describe positions of lane markings on both sides effectively when one side is occluded.
Vision-based Nighttime Vehicle Detection and Range Estimation for Driver Assistance
(2008)
Chen, Yen-Lin
;
Lin, Chuan-Tsai
;
Fan, Chung-Jui
;
Hsieh, Chih-Ming
;
Wu, Bing-Fei
This paper presents a real-time vision system for assisting driver during nighttime driving. The proposed system provides the following features: 1) Effectively detection and tracking of oncoming and preceding vehicles based on image segmentation and pattern analysis techniques. 2) Robust and adaptive vehicle detection under various illuminated conditions at nighttime urban environments benefited by a novel automatic object segmentation scheme. 3) Providing beneficial information for assisting the driver to perceive surrounding traffic conditions outside the car during nighttime driving. 4) Providing a versatile control strategy for in-vehicle facilities of the autonomous vehicles. 5) Offering real-time traffic event-driven video surveillance machinery for recording evidences of possible traffic accidents. Experimental results demonstrate the feasibility and effectiveness of the proposed system on nighttime driver assistance issues.