Title
Lane Detection Using Steerable Filters and FPGA-based Implementation
Abstract
Vision-based lane detection is a key component for Driver-Assistance (DA) systems. It is still a challenging task in road scenes with complex shadows. This paper presents a novel local edge detector, using vanishing point position as a high level information to guide the use of steerable flters in lane detection, and its implementation on a Field Programmable Gate Array (FPGA) device. The FPGA technology has the advantages of high-performances for digital image processing and low cost, both of which are the requirements of DA systems. The main contributions of this work are twofold: 1) an edge extraction algorithm for lane detection is proposed, using the estimated vanishing point as high-level information to detect lanes. Firstly, a rough estimation of the vanishing point is used for calculating the expected local edge orientations. Secondly, a steerable flter is tuned to the expected direction for edge response. 2) a framework on FPGA is designed to implement the proposed algorithm. The framework is designed by using multi-engine technology, so it works in parallel for any order of steerable flters. Experiments and comparisons show that the proposed algorithm is very effcient in dealing with the complex shadow conditions, and works in real-time on FPGA device.
Year
DOI
Venue
2011
10.1109/ICIG.2011.127
ICIG
Keywords
Field
DocType
fpga,vanishing point,feature extraction,field programmable gate arrays,computer vision,algorithm design and analysis,real time systems,convolution,edge detection,field programmable gate array,detectors,algorithm design
Computer vision,Algorithm design,Computer science,Edge detection,Field-programmable gate array,Feature extraction,Artificial intelligence,Digital image processing,Detector,Steerable filter,Vanishing point
Conference
Volume
Issue
Citations 
null
null
3
PageRank 
References 
Authors
0.42
10
4
Name
Order
Citations
PageRank
Er-Ke Shang1243.10
Jian Li261.53
Xiangjing An322612.15
Hangen He430723.86