Development of vision-based lane and vehicle detecting systems via the implementation with a dual-core DSP

Yeh, Chih-Hsien; Chen, Yung-Hsin

doi:10.1109/itsc.2006.1707382

Cited by 3 publications

(2 citation statements)

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“…The acquired images are, first, preprocessed in order to reduce the noise always caused by the sensors (Min, Liu, & Xu, 2006;Wennan, Qiang, & Hong, 2006;Ming-Dar, et al, 2008), to reduce the execution time based on the processing of a region of interest (Min, Liu, & Xu, 2006;Lim, Seng, Ngo, & Ang, 2009), or also to reduce the perspective effect of the lane by applying the inverse perspective mapping (Juan, Hilario, de la Escalera, & Armingol, 2005;Zu, 2006). The second step extracts the approximate pixels of LM either by detecting their edges (Min, Liu, & Xu, 2006;Ming-Dar, et al, 2008;Chih-Hsein & Chen, 2006;Haiping, Ko, Shil, Kim, & Kim, 2007), or by detecting their regions throw image segmentation (Lim, Seng, Ngo, & Ang, 2009;Serge, Michel, & Xuan, 1989;Chao, Mei, & D, 2010). The third step, the lane detection step, determines effective limits on an acquired image, based on the approximate pixels retained in the second step.…”

Section: Related Workmentioning

confidence: 99%

An Improved Lane Detection and Tracking Method for Lane Departure Warning Systems

Hammami

Romdhane

Ben-Abdallah

2013

International Journal of Computer Vision and Image Processing

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Lane detection and tracking are very crucial treatments in lane departure warning systems as they help the vehicle-mounted system to keep its lane. In this context, the authors’ work aims to develop vision-based lane detection and tracking method to detect and track lane limits in highways and main roads. The authors’ contribution focuses on the detection step. By exploiting the fact that, in an image, the road can be formed by linear and curvilinear portions, the authors propose two types of appropriate treatments to detect the lane limits. The authors’ method offers high precision rates independently of the painted lane marking’s characteristics, of the time of acquisition and in different weather conditions. Besides the challenges it overcomes, the authors’ method has the advantage of operating with a timing complexity that is reasonable for real-time applications. As shown experimentally, compared to three leading methods from the literature, the authors’ method has a higher efficiency.

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Section: Related Workmentioning

confidence: 99%

An Improved Lane Detection and Tracking Method for Lane Departure Warning Systems

Hammami

Romdhane

Ben-Abdallah

2013

International Journal of Computer Vision and Image Processing

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“…These windows are then filtered according to the coherence of their position and dimensions with respect to lane markings, before evaluating them with a more sophisticated detector. Alternatively, Chih-Hsien and Yung-Hsin (2006) and Sotelo et al (2005) used lane markings to guide the search for vehicles, processing frames in a more efficient and coherent manner.…”

Section: Combining Lane and Vehicle Detectionmentioning

confidence: 99%

On-board image-based vehicle detection and tracking

Ponsa

Serrat

López

2009

Transactions of the Institute of Measurement and Control

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In this paper we present a computer vision system for daytime vehicle detection and localization, an essential step in the development of several types of advanced driver assistance systems. It has a reduced processing time and high accuracy thanks to the combination of vehicle detection with lane-markings estimation and temporal tracking of both vehicles and lane markings. Concerning vehicle detection, our main contribution is a frame scanning process that inspects images according to the geometry of image formation, and with an Adaboost-based detector that is robust to the variability in the different vehicle types (car, van, truck) and lighting conditions. In addition, we propose a new method to estimate the most likely three-dimensional locations of vehicles on the road ahead. With regards to the lane-markings estimation component, we have two main contributions. First, we employ a different image feature to the other commonly used edges: we use ridges, which are better suited to this problem. Second, we adapt RANSAC, a generic robust estimation method, to fit a parametric model of a pair of lane markings to the image features. We qualitatively assess our vehicle detection system in sequences captured on several road types and under very different lighting conditions. The processed videos are available on a web page associated with this paper. A quantitative evaluation of the system has shown quite accurate results (a low number of false positives and negatives) at a reasonable computation time.

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