Browsing by Author "Tai, Jen-Chao"

Now showing 1 - 3 of 3

Dynamic calibration of pan-tilt-zoom cameras for traffic monitoring
(2006-10-01)
Song, Kai-Tai
;
Tai, Jen-Chao
;
電控工程研究所
;
Institute of Electrical and Control Engineering
Pan-tilt-zoom (PTZ) cameras have been widely used in recent years for monitoring and surveillance applications. These cameras provide flexible view selection as well as a wider observation range. This makes them suitable for vision-based traffic monitoring and enforcement systems. To employ PTZ cameras for image measurement applications, one first needs to calibrate the camera to obtain meaningful results. For instance, the accuracy of estimating vehicle speed depends on the accuracy of camera calibration and that of vehicle tracking results. This paper presents a novel calibration method for a PTZ camera overlooking, a traffic scene. The proposed approach requires no manual operation to select the positions of special features. It automatically uses a set of parallel lane markings and the lane width to compute the camera parameters, namely, focal length, tilt angle, and pan angle. Image processing procedures have been developed for automatically finding parallel lane markings. Interesting experimental results are presented to validate the robustness and accuracy of the proposed method.
IMAGE TRACKING OF MOTORCYCLES AND VEHICLES ON URBAN ROADS AND ITS APPLICATION TO TRAFFIC MONITORING AND ENFORCEMENT
(2010-09-01)
Tai, Jen-Chao
;
Song, Kai-Tai
;
電機工程學系
;
Department of Electrical and Computer Engineering
Image tracking has increasingly gained attention for use in vision-based traffic monitoring and surveillance applications. For many cities in Asia countries, it is desirable to detect multiple motorcycles as well as cars for urban traffic monitoring and enforcement. In this paper, a novel contour initialization and tracking algorithm is presented to track multiple motorcycles and vehicles at any position on the roadway. This method has the capability to detect moving vehicles of various sizes and to generate their initial contours for image tracking. The proposed method is not constrained by lane boundaries or vehicle size. To track vehicles on roadways, dynamic models are designed to predict the horizontal and vertical positions of vehicle contours. A Kalman filter is designed to update the prediction based on real-time image measurement. Practical experimental studies using video clips are presented to evaluate the performance of the proposed method. Traffic parameters such as traffic flow, vehicle speeds and traffic density are obtained with satisfactory accuracy.
Image-based traffic monitoring with shadow suppression
(2007-02-01)
Song, Kai-Tai
;
Tai, Jen-Chao
;
電控工程研究所
;
Institute of Electrical and Control Engineering
For a vision-based traffic monitoring and enforcement system, shadows of moving objects often cause serious errors in image analysis due to misclassification of shadows and moving vehicles. An effective shadow suppression method is thus required to improve the accuracy of image analysis and this paper proposes a novel color-space ratio model for detecting shadow pixels in traffic imagery. The proposed approach does not require many image sequences for constructing the model. Instead the model can be easily built up using a shadow region in a single image frame. To increase the accuracy of shadow detection, we design two types of spatial analysis to verify actual shadow pixels. Comparative results show that the proposed method works better than several well-known methods. The proposed methods have been applied to an image-based traffic monitoring system for detecting shadow pixels in traffic imagery. The experimental results not only validate the feasibility of the proposed algorithm but also successfully estimate traffic parameters such as traffic flows, traffic densities, vehicle turn ratios and vehicle speeds, all with satisfactory accuracy.