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

Liu, Wei; Wei, Longsheng; Li, Yongbo

doi:10.1155/2018/1547276

Cited by 4 publications

(5 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As shown in Figure 12, the left column images show the final fusion results by using the original affine matrix in [19] and the right column images are the results of the new deepaffine matrix in our method. The top three rows of images give the real occluded situation, showing that the occluded vehicles are blocked by other vehicles in road.…”

Section: Cooperative Visual Augmentation Results Based On Fusionmentioning

confidence: 99%

“…However, this method only make sense when the vehicles are both in the same lane. The authors of [19] introduced a method which can "see through" the forward vehicle by adopting affine transformation to fuse images from adjacent vehicles, no matter if they are in same lane or not. However, deviation in the occluded object's location and size always exists.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Liu

Gao

et al. 2021

Applied Sciences

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Cooperative Visual Augmentation Results Based On Fusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Liu

Gao

et al. 2021

Applied Sciences

Self Cite

View full text Add to dashboard Cite

show abstract

“…For Step 1, the set of evaluation elements was established according to the main infuences of each part on AEB [24,25] using the following formula:…”

Section: Clustering Variable Weightsmentioning

confidence: 99%

Research and Deduction of Car-to-TW Vehicle AEB Test Scenarios Based on Improved Clustering Methods

Dong,

Zhang,

Zhang

et al. 2023

Journal of Advanced Transportation

View full text Add to dashboard Cite

Two-wheeled (TW) vehicle accidents are one of the major types of urban traffic accidents. TW cyclists who lack safety protection usually suffer more serious injuries and deaths in collisions. Developments in automotive active safety technologies are expected to reduce cyclist injuries and deaths, such as automatic emergency braking (AEB). To facilitate the development and testing of AEB technology, typical TW vehicle scenarios need to be constructed. Based on 400 cases of car-to-TW vehicle accident data from the National Automobile Accident In-Depth Investigation System (NAIS) database, we investigated the scenario elements that influence AEB robustness, such as weather, accident time, and road wetness. We obtained seven static scenarios using an improved clustering method, and we obtained specific speed and distance combinations in each scenario using a deduction method. Further, we compared the present findings to those of other scholars and the China New Car Assessment Program (C-NCAP). The kinematic states of the two were similar to that of C-NCAP, but the speed distribution was significantly different. The TW vehicle speed in the C-NCAP is set to 15 km/h or 20 km/h concerning the European test scenarios, but the TW vehicle speed in the present study was 10–60 km/h. Thus, the present findings recommended that subsequent C-NCAP test scenarios increase the category of motorcycles and the speed range of cars covering 20–70 km/h and consider the test conditions of bad weather and wet roads, to test the robustness of AEB.

show abstract

“…From the above mathematical Eqs. (12) and 13, the mean square error 'MSE' is measured based on the cumulative squared error between the actual image size 'α' and preprocessed image size 'α 0 i '. With the help of estimated mean square error using Eq.…”

Section: A Peak Signal To Noise Ratiomentioning

confidence: 99%

“…A color cubic feature called color cubic local binary pattern (CC-LBP) was presented in [12] to discover multiclass partial occluded traffic signs with high accuracy in real-time. However, the computation complexity was higher.…”

Section: Introductionmentioning

confidence: 99%

Non-linear Sorenson–Dice Exemplar Image Inpainting Based Bayes Probability for Occlusion Removal in Remote Traffic Control

Arun

Kumar

2021

Multimed Tools Appl

View full text Add to dashboard Cite

Occlusion removal is a significant problem to be resolved in a remote traffic control system to enhance road safety. However, the conventional techniques do not recognize traffic signs well due to the vehicles are occluded. Besides occlusion removal was not performed in existing techniques with a less amount of time. In order to overcome such limitations, Non-linear Gaussian Bilateral Filtered Sorenson–Dice Exemplar Image Inpainting Based Bayes Conditional Probability (NGBFSEII-BCP) Method is proposed. Initially, a number of remote sensing images are taken as input from Highway Traffic Dataset. Then, the NGBFSEII-BCP method applies the Non-Linear Gaussian Bilateral Filtering (NGBF) algorithm for removing the noise pixels in input images. After preprocessing, the NGBFSEII-BCP method is used to remove the occlusion in the input images. Finally, NGBFSEII-BCP Method applies Bayes conditional probability to find operation status and thereby gets higher road safety using remote sensing images. The technique conducts the simulation evaluation using metrics such as peak signal to noise ratio, computational time, and detection accuracy. The simulation result illustrates that the NGBFSEII-BCP Method increases the detection accuracy by 20% and reduces the computation time by 32% as compared to state-of-the-art works.

show abstract

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

Cited by 4 publications

References 22 publications

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

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

Research and Deduction of Car-to-TW Vehicle AEB Test Scenarios Based on Improved Clustering Methods

Non-linear Sorenson–Dice Exemplar Image Inpainting Based Bayes Probability for Occlusion Removal in Remote Traffic Control

Contact Info

Product

Resources

About