Monocular parking slots and obstacles detection and tracking

Allodi, Marco; Castangia, Luca; Cionini, Alessandro; Valenti, Francesco

doi:10.1109/ivs.2016.7535383

Cited by 5 publications

(1 citation statement)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Both approaches generate the line response map by using the characteristics of a line with a convex intensity profile distinct from the background of the image. The line is then detected from the line response map using a random sample consensus (RANSAC) [ 8 , 9 , 11 , 12 , 17 , 18 ], Hough transform [ 3 , 4 , 5 , 6 , 7 , 16 , 19 , 20 ], or Radon transform [ 2 , 13 ]. The process described above has been successful in many line detection studies.…”

Section: Introductionmentioning

confidence: 99%

Specular Detection on Glossy Surface Using Geometric Characteristics of Specularity in Top-View Images

Kim

Ra²,

Kim

2021

Sensors

View full text Add to dashboard Cite

In an autonomous driving assistance system (ADAS), top views effectively represent objects around the vehicle on a 2D plane. Top-view images are therefore widely used to detect lines in ADAS applications such as lane-keeping assistance and parking assistance. Because line detection is a crucial step for these applications, the false positive detection of lines can lead to failure of the system. Specular reflections from a glossy surface are often the cause of false positives, and since certain specular patterns resemble actual lines in the top-view image, their presence induces false positive lines. Incorrect positions of the lines or parking stalls can thus be obtained. To alleviate this problem, we propose two methods to estimate specular pixels in the top-view image. The methods use a geometric property of the specular region: the shape of the specular region is stretched long in the direction of the camera as the distance between the camera and the light source becomes distant, resulting in a straight line. This property can be used to distinguish the specular region in images. One estimates the pixel-wise probability of the specularity using gradient vectors obtained from an edge detector and the other estimates specularity using the line equation of each line segment obtained by line detection. To evaluate the performance of the proposed method, we added our methods as a pre-processing step to existing parking stall detection methods and investigated changes in their performance. The proposed methods improved line detection performance by accurately estimating specular components in the top-view images.

show abstract