On the applicability of inverse perspective mapping for the forward distance estimation based on the HSV colormap

Adamshuk, Rodrigo; Carvalho, David Ferreira; Neme, João Henrique Zander; Margraf, Erick; Okida, Sérgio; Tusset, Ângelo Marcelo; Santos, Max Mauro Dias; Amaral, Rodrigo; Ventura, Artur; Carvalho, Saulo

doi:10.1109/icit.2017.7915504

Cited by 14 publications

(10 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most of the existing vehicle ranging methods rely on camera imaging principle to estimate the distance, mainly divided into two types: one based on the vehicle position [8,[29][30][31] and one based on the vehicle width [30,32]. The location-based ranging model assumes that the road is flat and sensitive to noise.…”

Section: Fusion Rangingmentioning

confidence: 99%

“…The distance estimation method based on monocular vision mainly uses the camera pinhole model or inverse perspective mapping (IMP). Adamshuk et al proposed a distance estimation method based on IPM in the HSV color map, which used the linear relationship between the transformed image pixels and the actual distance [29]. Han et al proposed calculating the distance based on vehicle width estimated by using the lane line and considered the situation without the lane line, but there is a big error in estimating the width of the lane line and target vehicle [30].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Vehicle Detection and Ranging Using Two Different Focal Length Cameras

Liu

Zhang

2020

Journal of Sensors

View full text Add to dashboard Cite

Vehicle detection is a crucial task for autonomous driving and demands high accuracy and real-time speed. Considering that the current deep learning object detection model size is too large to be deployed on the vehicle, this paper introduces the lightweight network to modify the feature extraction layer of YOLOv3 and improve the remaining convolution structure, and the improved Lightweight YOLO network reduces the number of network parameters to a quarter. Then, the license plate is detected to calculate the actual vehicle width and the distance between the vehicles is estimated by the width. This paper proposes a detection and ranging fusion method based on two different focal length cameras to solve the problem of difficult detection and low accuracy caused by a small license plate when the distance is far away. The experimental results show that the average precision and recall of the Lightweight YOLO trained on the self-built dataset is 4.43% and 3.54% lower than YOLOv3, respectively, but the computing speed of the network decreases 49 ms per frame. The road experiments in different scenes also show that the long and short focal length camera fusion ranging method dramatically improves the accuracy and stability of ranging. The mean error of ranging results is less than 4%, and the range of stable ranging can reach 100 m. The proposed method can realize real-time vehicle detection and ranging on the on-board embedded platform Jetson Xavier, which satisfies the requirements of automatic driving environment perception.

show abstract

Section: Fusion Rangingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vehicle Detection and Ranging Using Two Different Focal Length Cameras

Liu

Zhang

2020

Journal of Sensors

View full text Add to dashboard Cite

show abstract

“…This length is proportional to cosine

and maximized when

is zero; this represents the top-view image. Because the inverse perspective mapping removes a perspective effect caused by different depths of objects [ 39 ], the specular region which looks like a slightly stretched ellipse in the oblique-view image appears as a very long straight line in the top-view image.…”

Section: Proposed Methodsmentioning

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

“…In recent years, research on the methods for estimating the distance of monocular vision vehicles based on geometric models, which are largely divided into the inverse perspective mapping transformation method [3], projection geometric relationship method [5], and fitting modeling method [16], has achieved considerable results. In [4], [17], [18], [20], a distance estimation method based on IPM was proposed. The difference among these studies is the object detection method, such as the road removal algorithm [4], [17], threshold adjustment [18], and hue, saturation, and value (HSV) color mapping [20].…”

Section: Related Workmentioning

confidence: 99%

Robust Inter-Vehicle Distance Estimation Method Based on Monocular Vision

Huang

Zhe

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Advanced driver assistance systems (ADAS) based on monocular vision are rapidly becoming a popular research subject. In ADAS, inter-vehicle distance estimation from an in-car camera based on monocular vision is critical. At present, related methods based on a monocular vision for measuring the absolute distance of vehicles ahead experience accuracy problems in terms of the ranging result, which is low, and the deviation of the ranging result between different types of vehicles, which is large and easily affected by a change in the attitude angle. To improve the robustness of a distance estimation system, an improved method for estimating the distance of a monocular vision vehicle based on the detection and segmentation of the target vehicle is proposed in this paper to address the vehicle attitude angle problem. The angle regression model (ARN) is used to obtain the attitude angle information of the target vehicle. The dimension estimation network determines the actual dimensions of the target vehicle. Then, a 2D base vector geometric model is designed in accordance with the image analytic geometric principle to accurately recover the back area of the target vehicle. Lastly, area-distance modeling based on the principle of camera projection is performed to estimate distance. The experimental results on the real-world computer vision benchmark, KITTI, indicate that our approach achieves superior performance compared with other existing published methods for different types of vehicles (including front and sideway vehicles).

show abstract

On the applicability of inverse perspective mapping for the forward distance estimation based on the HSV colormap

Cited by 14 publications

References 4 publications

Vehicle Detection and Ranging Using Two Different Focal Length Cameras

Vehicle Detection and Ranging Using Two Different Focal Length Cameras

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

Robust Inter-Vehicle Distance Estimation Method Based on Monocular Vision

Contact Info

Product

Resources

About