Front Vehicle Blind Spot Translucentization Based on Augmented Reality

Lin, Chien-Chung; Lin, Yu‐Chen; Chen, Long-Tai; Wang, Yuan-Fang

doi:10.1109/vtcfall.2013.6692210

Cited by 3 publications

(12 citation statements)

References 11 publications

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“…Hence, the mapping parameters between two images need to be estimated. The work [17] used polar coordinates to approximately simulate the mapping relation between two images in a global, linear form to get visually appealing color fusion result. This method can achieve good performance in vehicles in the same lane (shown in Figure 1(a)) but failed in vehicles in different lane (shown in Figure 1(b)).…”

Section: Object-based Data Fusionmentioning

confidence: 99%

“…Hence, RANSAC is also used to optimize the parameter of the transformation. Affine transformation can offer better performance than linear method in [17], but the projection of the objects from image B to image A is still not precisely correct. Because we use matching points and of object G to estimate the parameters of affine transformation of points and of object F, this rough method can only be applied to realize visualization, which helps drivers to "see" objects occluded and perceive the approximate situation ahead of the front vehicles.…”

Section: Object-based Data Fusionmentioning

confidence: 99%

“…Experiments were performed on dataset from [17], Karlsruhe dataset, KITTI dataset, and our own dataset. Four Datasets were shown in Figure 6.…”

Section: Datasetsmentioning

confidence: 99%

“…Their method need accurate distance information gathered from radar sensor to realize the object projection between two images. The work [17] proposes a vehicle blind spot elimination system based on videos captured from other vehicles. But the linear projection fusion causes superior deformation when front and back vehicles are in different lanes.…”

Section: Introductionmentioning

confidence: 99%

“…And there is no limitation of the location of front and back vehicles. The main idea is inspired from [6,17]. The main contributions of this paper are as follows.…”

Section: Introductionmentioning

confidence: 99%

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Occluded Street Objects Perception Algorithm of Intelligent Vehicles Based on 3D Projection Model

Liu

Wei

2018

Journal of Advanced Transportation

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We present a super perception system of intelligent vehicles for perceiving occluded street objects by collecting images from neighbor front vehicles V2V (vehicle to vehicle) video streams based on 3D projection model. This super power can avoid some serious accidents of driver-assistant systems or automatic driving systems which can only detect visible objects. Our street perception system can "see through" the front vehicles to realize detecting of the occluded street objects only by analyzing the pair images received from front and host (back) vehicles. Upon the 3D projection model based on the pair images, the system uses affine transformation to achieve augmented reality method to increase the visibility perspective of driver system. Experimental results on different datasets are shown to validate our approach. Evaluation method was first introduced into our perception system.

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Section: Object-based Data Fusionmentioning

confidence: 99%

Section: Object-based Data Fusionmentioning

confidence: 99%

“…Experiments were performed on dataset from [17], Karlsruhe dataset, KITTI dataset, and our own dataset. Four Datasets were shown in Figure 6.…”

Section: Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…And there is no limitation of the location of front and back vehicles. The main idea is inspired from [6,17]. The main contributions of this paper are as follows.…”

Section: Introductionmentioning

confidence: 99%

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Occluded Street Objects Perception Algorithm of Intelligent Vehicles Based on 3D Projection Model

Liu

Wei

2018

Journal of Advanced Transportation

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Vehicle Blind Spot Area Detection Using Bluetooth Low Energy and Multilateration

Pratama

Abdurohman

Putrada

2021

2021 9th International Conference on Information and Communication Technology (ICoICT)

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Cooperative Visual Augmentation Algorithm of Intelligent Vehicle Based on Inter-Vehicle Image Fusion

Liu

Gao

et al. 2021

Applied Sciences

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In a connected vehicle environment based on vehicle-to-vehicle (V2V) technology, images from front and ego vehicles are fused to augment a driver’s or autonomous system’s visual field, which is helpful in avoiding road accidents by eliminating the blind point (the objects occluded by vehicles), especially tailgating in urban areas. Realizing multi-view image fusion is a tough problem without knowing the relative location of two sensors and the fusing object is occluded in some views. Therefore, we propose an image geometric projection model and a new fusion method between neighbor vehicles in a cooperative way. Based on a 3D inter-vehicle projection model, selected feature matching points are adopted to estimate the geometric transformation parameters. By adding deep information, our method also designs a new deep-affine transformation to realize fusing of inter-vehicle images. Experimental results on KIITI (Karlsruhe Institute of Technology and Toyota Technological Institute) datasets are shown to validate our algorithm. Compared with previous work, our method improves the IoU index by 2~3 times. This algorithm can effectively enhance the visual perception ability of intelligent vehicles, and it will help to promote the further development and improvement of computer vision technology in the field of cooperative perception.

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Front Vehicle Blind Spot Translucentization Based on Augmented Reality

Cited by 3 publications

References 11 publications

Occluded Street Objects Perception Algorithm of Intelligent Vehicles Based on 3D Projection Model

Occluded Street Objects Perception Algorithm of Intelligent Vehicles Based on 3D Projection Model

Vehicle Blind Spot Area Detection Using Bluetooth Low Energy and Multilateration

Cooperative Visual Augmentation Algorithm of Intelligent Vehicle Based on Inter-Vehicle Image Fusion

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