Efficient vanishing point detection method in unstructured road environments based on dark channel prior

Ding, Weiliang; Li, Yong; Liu, Honghai

doi:10.1049/iet-cvi.2015.0390

Cited by 17 publications

(9 citation statements)

References 24 publications

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“…In [16], the J-Linkage algorithm was used to compute a set of VP candidates from detected edges, and final VP was refined using EM algorithm. In [17] and [18], the highway scene was divided into rough road region, sky region and vertical region with a dark channel prior based segmentation method and vertical envelope lines analysis. Then, road lines were extracted with several own-defined constraints and VP was estimated through mean-shift clustering.…”

Section: A Vp Detectionmentioning

confidence: 99%

Vanishing Point Detection and Rail Segmentation Based on Deep Multi-Task Learning

Zhu

et al. 2020

IEEE Access

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In modern railway systems, video surveillance and machine vision analysis have been widely used to detect perimeter intrusions. For pan-tilt-zoom (PTZ) cameras, the machine vision system needs to detect adjustments in PTZ cameras and then automatically determine the new alarm region in real time. In this paper, we propose a deep multi-task learning based algorithm for simultaneous vanishing point (VP) detection and rail segmentation, which can identify camera adjustment from changes in VP, and then automatically determine the alarm region from segmented rails. The multi-task based neural network consists of a feature extraction base network and three sub-task networks. The first sub-task network is a convolution regression network for VP detection. The second sub-task network utilizes an encoder-decoder structure for vanishing region (VR, a fixed region centered on VP) segmentation. The third sub-task network shares the encoder-decoder structure with the VR segmentation task and is used for rail segmentation. The VR segmentation task is activated only at the training stage, serving as an auxiliary task to enhance feature learning ability and increase VP detection accuracy. To further improve the accuracies of VP detection and rail segmentation, low-level features is modulated by high-level semantic information before feeding to the decoder stage. With the help of shared feature extraction and auxiliary training, the proposed VP prediction method needs very small training dataset and outperforms other methods in both efficiency and accuracy. INDEX TERMS vanishing point detection, rail segmentation, intrusion detection, multi-task learning, deep learning.

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Section: A Vp Detectionmentioning

confidence: 99%

Vanishing Point Detection and Rail Segmentation Based on Deep Multi-Task Learning

Zhu

et al. 2020

IEEE Access

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“…In this approach, after obtaining line features using Hough transform, the vanishing point will be the point at which the majority of the supporting line segment primitives intersect. The second method considers vanishing point computation as a statistical estimation problem [9,21,22,25,26]. In most cities, a pedestrian crossing is characterized by a set of parallel stripes.…”

Section: Searching For Vanishing Pointsmentioning

confidence: 99%

A Coarse-to-Fine Framework for Multiple Pedestrian Crossing Detection

Sun

Zhao

2020

Sensors

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When providing route guidance to pedestrians, one of the major safety considerations is to ensure that streets are crossed at places with pedestrian crossings. As a result, map service providers are keen to gather the location information about pedestrian crossings in the road network. Most, if not all, literature in this field focuses on detecting the pedestrian crossing immediately in front of the camera, while leaving the other pedestrian crossings in the same image undetected. This causes an under-utilization of the information in the video images, because not all pedestrian crossings captured by the camera are detected. In this research, we propose a coarse-to-fine framework to detect pedestrian crossings from probe vehicle videos, which can then be combined with the GPS traces of the corresponding vehicles to determine the exact locations of pedestrian crossings. At the coarse stage of our approach, we identify vanishing points and straight lines associated with the stripes of pedestrian crossings, and partition the edges to obtain rough candidate regions of interest (ROIs). At the fine stage, we determine whether these candidate ROIs are indeed pedestrian crossings by exploring their prior constraint information. Field experiments in Beijing and Shanghai cities show that the proposed approach can produce satisfactory results under a wide variety of situations.

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“…Recently, the number of geometrical feature-based methods for R-VP detection increased significantly. These recent existing methods can be classified into four groups based on types of geometrical features used in the methods, such as line segment-based [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ], edge-based [ 30 , 31 , 32 , 33 ], motion-based [ 34 , 35 , 36 , 37 ], and texture-based methods [ 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 ].…”

Section: Introductionmentioning

confidence: 99%

A Robust Road Vanishing Point Detection Adapted to the Real-world Driving Scenes

Khac

Choi

Park

et al. 2021

Sensors

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Vanishing point (VP) provides extremely useful information related to roads in driving scenes for advanced driver assistance systems (ADAS) and autonomous vehicles. Existing VP detection methods for driving scenes still have not achieved sufficiently high accuracy and robustness to apply for real-world driving scenes. This paper proposes a robust motion-based road VP detection method to compensate for the deficiencies. For such purposes, three main processing steps often used in the existing road VP detection methods are carefully examined. Based on the analysis, stable motion detection, stationary point-based motion vector selection, and angle-based RANSAC (RANdom SAmple Consensus) voting are proposed. A ground-truth driving dataset including various objects and illuminations is used to verify the robustness and real-time capability of the proposed method. The experimental results show that the proposed method outperforms the existing motion-based and edge-based road VP detection methods for various illumination conditioned driving scenes.

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Efficient vanishing point detection method in unstructured road environments based on dark channel prior

Cited by 17 publications

References 24 publications

Vanishing Point Detection and Rail Segmentation Based on Deep Multi-Task Learning

Vanishing Point Detection and Rail Segmentation Based on Deep Multi-Task Learning

A Coarse-to-Fine Framework for Multiple Pedestrian Crossing Detection

A Robust Road Vanishing Point Detection Adapted to the Real-world Driving Scenes

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