1,034 research outputs found
Multiple Lane Detection Algorithm Based on Optimised Dense Disparity Map Estimation
Lane detection is very important for self-driving vehicles. In recent years,
computer stereo vision has been prevalently used to enhance the accuracy of the
lane detection systems. This paper mainly presents a multiple lane detection
algorithm developed based on optimised dense disparity map estimation, where
the disparity information obtained at time t_{n} is utilised to optimise the
process of disparity estimation at time t_{n+1}. This is achieved by estimating
the road model at time t_{n} and then controlling the search range for the
disparity estimation at time t_{n+1}. The lanes are then detected using our
previously published algorithm, where the vanishing point information is used
to model the lanes. The experimental results illustrate that the runtime of the
disparity estimation is reduced by around 37% and the accuracy of the lane
detection is about 99%.Comment: 5 pages, 7 figures, IEEE International Conference on Imaging Systems
and Techniques (IST) 201
A Novel Disparity Transformation Algorithm for Road Segmentation
The disparity information provided by stereo cameras has enabled advanced
driver assistance systems to estimate road area more accurately and
effectively. In this paper, a novel disparity transformation algorithm is
proposed to extract road areas from dense disparity maps by making the
disparity value of the road pixels become similar. The transformation is
achieved using two parameters: roll angle and fitted disparity value with
respect to each row. To achieve a better processing efficiency, golden section
search and dynamic programming are utilised to estimate the roll angle and the
fitted disparity value, respectively. By performing a rotation around the
estimated roll angle, the disparity distribution of each row becomes very
compact. This further improves the accuracy of the road model estimation, as
demonstrated by the various experimental results in this paper. Finally, the
Otsu's thresholding method is applied to the transformed disparity map and the
roads can be accurately segmented at pixel level.Comment: 16 pages, 8 figure
Real-Time Stereo Vision for Road Surface 3-D Reconstruction
Stereo vision techniques have been widely used in civil engineering to
acquire 3-D road data. The two important factors of stereo vision are accuracy
and speed. However, it is very challenging to achieve both of them
simultaneously and therefore the main aim of developing a stereo vision system
is to improve the trade-off between these two factors. In this paper, we
present a real-time stereo vision system used for road surface 3-D
reconstruction. The proposed system is developed from our previously published
3-D reconstruction algorithm where the perspective view of the target image is
first transformed into the reference view, which not only increases the
disparity accuracy but also improves the processing speed. Then, the
correlation cost between each pair of blocks is computed and stored in two 3-D
cost volumes. To adaptively aggregate the matching costs from neighbourhood
systems, bilateral filtering is performed on the cost volumes. This greatly
reduces the ambiguities during stereo matching and further improves the
precision of the estimated disparities. Finally, the subpixel resolution is
achieved by conducting a parabola interpolation and the subpixel disparity map
is used to reconstruct the 3-D road surface. The proposed algorithm is
implemented on an NVIDIA GTX 1080 GPU for the real-time purpose. The
experimental results illustrate that the reconstruction accuracy is around 3
mm.Comment: 6 pages, 4 figures, IEEE International Conference on Imaging System
and Techniques (IST) 2018. arXiv admin note: substantial text overlap with
arXiv:1807.0204
Real-Time Stereo Vision-Based Lane Detection System
The detection of multiple curved lane markings on a non-flat road surface is
still a challenging task for automotive applications. To make an improvement,
the depth information can be used to greatly enhance the robustness of the lane
detection systems. The proposed system in this paper is developed from our
previous work where the dense vanishing point Vp is estimated globally to
assist the detection of multiple curved lane markings. However, the outliers in
the optimal solution may severely affect the accuracy of the least squares
fitting when estimating Vp. Therefore, in this paper we use Random Sample
Consensus to update the inliers and outliers iteratively until the fraction of
the number of inliers versus the total number exceeds our pre-set threshold.
This significantly helps the system to overcome some suddenly changing
conditions. Furthermore, we propose a novel lane position validation approach
which provides a piecewise weight based on Vp and the gradient to reduce the
gradient magnitude of the non-lane candidates. Then, we compute the energy of
each possible solution and select all satisfying lane positions for
visualisation. The proposed system is implemented on a heterogeneous system
which consists of an Intel Core i7-4720HQ CPU and a NVIDIA GTX 970M GPU. A
processing speed of 143 fps has been achieved, which is over 38 times faster
than our previous work. Also, in order to evaluate the detection precision, we
tested 2495 frames with 5361 lanes from the KITTI database (1637 lanes more
than our previous experiment). It is shown that the overall successful
detection rate is improved from 98.7% to 99.5%.Comment: 24 pages, 10 figure
- …