Browsing by Author "Fan, Chung-Jui"

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A new vehicle detection approach in traffic jam conditions
(2007)
Wu, Bing-Fei
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Juang, Jhy-Hong
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Tsai, Ping-Tsung
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Chang, Ming-Wei
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Fan, Chung-Jui
The well-known vehicle detectors utilize the background extraction methods to segment the moving objects. The back-ground updating concept is applied to overcome the luminance variation which results in the error detection. These systems will meet a challenge when detecting the vehicles in the traffic jam conditions at sunset. The vehicles will cover the road surface so that the background information cannot be smoothly updated. Once the traffic is released, the existing background is not suitable for the moving segmentation. The main contribution of this paper is that an efficient vehicle detection approach is proposed to improve the detection accuracy in traffic jam conditions. The land mask decision gives the land information and the merged boundary box rule is presented to realize vehicle detection. The signed square normalized correlation coefficient calculation is addressed, and it is applied to vehicle tracking. The experimental results show that this approach works well in highway and urban area with high accuracy.
A Real-Time Vision System for Nighttime Vehicle Detection and Traffic Surveillance
(2011-05-01)
Chen, Yen-Lin
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Wu, Bing-Fei
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Huang, Hao-Yu
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Fan, Chung-Jui
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電控工程研究所
This paper presents an effective traffic surveillance system for detecting and tracking moving vehicles in nighttime traffic scenes. The proposed method identifies vehicles by detecting and locating vehicle headlights and taillights using image segmentation and pattern analysis techniques. First, a fast bright-object segmentation process based on automatic multilevel histogram thresholding is applied to effectively extract bright objects of interest. This automatic multilevel thresholding approach provides a robust and adaptable detection system that operates well under various nighttime illumination conditions. The extracted bright objects are then processed by a spatial clustering and tracking procedure that locates and analyzes the spatial and temporal features of vehicle light patterns, and identifies and classifies moving cars and motorbikes in traffic scenes. The proposed real-time vision system has also been implemented and evaluated on a TI DM642 DSP-based embedded platform. The system is set up on elevated platforms to perform traffic surveillance on real highways and urban roads. Experimental results demonstrate that the proposed traffic surveillance approach is feasible and effective for vehicle detection and identification in various nighttime environments.
Real-time Vision-based Multiple Vehicle Detection and Tracking for Nighttime Traffic Surveillance
(2009)
Chen, Yen-Lin
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Wu, Bing-Fei
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Fan, Chung-Jui
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電控工程研究所
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Institute of Electrical and Control Engineering
This study presents an effective system for detecting and tracking moving vehicles in nighttime traffic scene for traffic surveillance. The proposed method identifies vehicles based on detecting and locating vehicle headlights and taillights by using the techniques of image segmentation and pattern analysis. First, to effectively extract bright objects of interest, a fast bright-object segmentation process based on automatic multilevel histogram thresholding is applied on the nighttime road-scene images. This automatic multilevel thresholding approach can provide robustness and adaptability for the detection system to be operated well under various illumination conditions at night. The extracted bright objects are processed by a spatial clustering and tracking procedure by locating and analyzing the spatial and temporal features of vehicle light patterns, and then identifying and classifying the moving cars and motorbikes in the traffic scenes. Experimental results demonstrate that the proposed approach is feasible and effective for vehicle detection and identification in various nighttime environments for traffic surveillance.
The Vision-based Vehicle Detection and Incident Detection System in Hsueh-Shan Tunnel
(2008)
Wu, Bing-Fei
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Kao, Chih-Chung
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Liu, Chih-Chun
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Fan, Chung-Jui
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Chen, Chao-Jung
The automatic lane marking detection, vehicle detection and incident detection systems are proposed in this paper. The block-based background extraction that combines statistical algorithm and the moving block information is used to obtain the color background image more exactly. The lane detection algorithm is applied to obtain the lane information from the color background image without the limitation of the camera setting. The presented vehicle detection algorithm is a block-based approach which is widely used in highway systems, urban roadways or tunnels. Different applications adopt the different detection zones where the vehicles will be tracked and identified in. The presented incident detection algorithm focuses on the car stopped, vehicle lane changing, and congestion condition, and the experimental results in Hsueh-Shan tunnel are addressed, which also demonstrate the stability and the effectiveness of the proposed methods.
Vision-based Nighttime Vehicle Detection and Range Estimation for Driver Assistance
(2008)
Chen, Yen-Lin
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Lin, Chuan-Tsai
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Fan, Chung-Jui
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Hsieh, Chih-Ming
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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.