Generalizing Laplacian of Gaussian Filters for Vanishing-Point Detection

Kong, Hui; Sarma, Sanjay E.; Tang, Fan

doi:10.1109/tits.2012.2216878

Cited by 82 publications

(91 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most of these images were used in the papers of Kong et al, [3] and Lu [4]. Of the images, 157 images are unstructured road scenes and the remaining images are general urban road scenes or urban road scenes containing vehicles and pedestrians.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Efficient vanishing point detection method in complex urban road environments

Ding

2015

IET Computer Vision

View full text Add to dashboard Cite

Detecting the vanishing point in a road image is important for robot navigation, intelligent transportation and other fields. This study proposes a new vanishing point detection method that uses the vertical information in complex urban road and street environments. First, all straight lines in the road image are detected based on curvature scale space and principal component analysis methods. Second, a new road region extraction method is proposed to support vanishing point detection. In this method, the image is decomposed into approximate sky, vertical and road regions using the estimation envelopes of the vertical lines. Third, the straight lines in the road region are extracted using a path perspective triangle and line length limits. The straight lines are then categorised into groups using proposed grouping strategies. Finally, vanishing point candidates are calculated from paired lines extracted from different groups and from the same group, and the final vanishing point is then estimated for the urban road scene using the mean shift clustering method. The experimental results show that the proposed algorithm can estimate the vanishing point accurately and efficiently in complex urban road environments, despite interference from vehicles and pedestrians and on curved and unstructured roads.

show abstract

Section: Resultsmentioning

confidence: 99%

“…The table also shows that the estimation error is minimum when σ θ = 50 and ε θ = 8. The parameters σ θ and ε θ have little influence on the vanishing point estimation when σ θ ∈ [50, 60] and ε θ ∈ [3,8]; consequently, these parameter ranges are suitable for the vanishing point estimation.…”

Section: Parameter Sensitivitymentioning

confidence: 99%

Efficient vanishing point detection method in complex urban road environments

Ding

2015

IET Computer Vision

View full text Add to dashboard Cite

show abstract

“…Kong et al [25] used directional information from an image texture to detect the VP. Eight disjointed components with similar directions were detected in the image, including the point where most straight-line intersections occurred.…”

Section: Introductionmentioning

confidence: 99%

Efficient Vanishing Point Detection for Driving Assistance Based on Visual Saliency Map and Image Segmentation from a Vehicle Black-Box Camera

Kim

2019

Symmetry

View full text Add to dashboard Cite

Techniques for detecting a vanishing point (VP) which estimates the direction of a vehicle by analyzing its relationship with surrounding objects have gained considerable attention recently. VPs can be used to support safe vehicle driving in areas such as for autonomous driving, lane-departure avoidance, distance estimation, and road-area detection, by detecting points in which parallel extension lines of objects are concentrated at a single point in a 3D space. In this paper, we proposed a method of detecting the VP in real time for applications to intelligent safe-driving support systems. In order to support safe driving of autonomous vehicles, it is necessary to drive the vehicle with the VP in center of the road image in order to prevent the vehicle from moving out of the road area while driving. Accordingly, in order to detect the VP in the road image, a method of detecting a point where straight lines intersect in an area where edge directional feature information is concentrated is required. The visual attention model and image segmentation process are applied to quickly identify candidate VPs in the area where the edge directional feature-information is concentrated and the intensity contrast difference is large. In the proposed method, VPs are detected by analyzing the edges, visual-attention regions, linear components using the Hough transform, and image segmentation results in an input image. Our experimental results have shown that the proposed method could be applied to safe-driving support systems.

show abstract

“…Image edges carry the structure information of the whole scene, playing the key role in feature representation. Lots of researches about edge detection have been done [1]- [6]. The basic process of image detection is to evaluate the neighborhood of the pixel and then get the local maxima.…”

Section: Introductionmentioning

confidence: 99%

“…The evaluation is usually carried out by a derivative based template. Some classical approaches used a fixed filter template, such as Sobel [5], LOG [6]. Others used a dynamic template size, such as Canny operator [1].…”

Section: Introductionmentioning

confidence: 99%