Browsing by Author "Chen, Yuan-Hsin"

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A real-time multiple-vehicle detection and tracldng system with prior occlusion detection and resolution, and prior queue detection and resolution
(2006)
Lin, Shin-Ping
;
Chen, Yuan-Hsin
;
Wu, Bing-Fei
;
電控工程研究所
;
Institute of Electrical and Control Engineering
The proposed multiple-vehicle detection and tracking (MVDT) system utilizes a color background to segment moving objects and exploits relations among the moving objects and existed trajectories to track vehicles. Initially, the background is extracted by classification. Then, it is regularly updated by previous moving objects to guarantee robust segmentation in luminance-change circumstance. For partial wrong converged background due to roadside parking vehicles, it will be corrected later by checking fed back trajectories to avoid false detection after the vehicles moving away. In tracking processing, the relations of distances or distances and angles are applied to determine whether to create, extend, and delete a trajectory. If occlusion detected after trajectory creation, it will be resolved by rule-based tracking reasoning. Otherwise, lane information will be used. In addition, to further resolve occlusion occurred in lane, a queue detection and resolution technique will be utilized prior to tracking processing. Finally, for easy setup, parameter automation for the system is proposed.
Nighttime vehicle detection for driver assistance and autonomous vehicles
(2006)
Chen, Yen-Lin
;
Chen, Yuan-Hsin
;
Chen, Chao-Jung
;
Wu, Bing-Fei
;
電控工程研究所
This study presents an effective method for detecting vehicles in front of the camera-assisted car during nighttime driving. The proposed method detects vehicles based on detecting and locating vehicle headlights and taillights using techniques of image segmentation and pattern analysis. First, to effectively extract bright objects of interest, a segmentation process based on automatic multilevel thresholding is applied on the grabbed road-scene images. Then the extracted bright objects are processed by a rule-based procedure, to identify the vehicles by locating and analyzing their vehicle light patterns, and estimate their distances to the camera-assisted car. Experimental results demonstrate the effectiveness of the proposed method on detecting vehicles at night.