Visibility Detection Method Based on Camera Model Calibration

Zou, Jie

doi:10.1109/icisce.2017.165

Cited by 6 publications

(3 citation statements)

References 5 publications

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“…Therefore, to improve the accuracy of visibility estimation, He [15] added a bootstrap filtering technique after finding the transmittance to further optimize the image transmittance values. For a more accurate estimation of visibility, Zou [16] proposed a visibility estimation method based on camera calibration. This method estimates the visibility by using nonlinear least squares method to fit the relationship between the eigenvalues and the actual distance from the input image.…”

Section: A Visibility Estimation Methods Based On Physical Modelmentioning

confidence: 99%

FGS-Net: A Visibility Estimation Method Based on Statistical Feature Stream in Fog Area

Liu

Zhong

et al. 2023

Preprint

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Low visibility always leads to serious traffic accidents worldwide, and it remains a tough problem despite the extensive work researched in the field of meteorology. Therefore, we propose a novel end-to-end network, FGS-Net, for visibility estimation, combining "engineered features" and "learned features" to achieve higher accuracy. Specifically, we propose a novel and effective fog region segmentation method, named ASRS(Auto Seed Region Segmentation), to segment the fog regions in the input image. Subsequently, two "specific features" (transmittance matrix, dark channel matrix) and three "common features" (contrast, average gradient, brightness) are extracted from the fog region. Next, the feature information of the fog region is statistically calculated as "engineering features" for visibility estimation. In addition, our approach uses Transformer, a classical model commonly used in the field of Natural Language Processing (NLP) to obtain the "learned features" for visibility estimation. Furthermore, in order to obtain more efficient "learned features", we embed the Coordinate Attention (CA) module in FGS-Net. Finally, to verify the effectiveness and superiority of our method, We evaluate our approach on two visibility datasets: Visibility Image Dataset Ⅰ (VID I) and Visibility Image Dataset Ⅱ (VID II), where VID I is a real scene visibility dataset and VID II is a synthetic visibility dataset. The experimental results show that our method has better performance than the classical one on these two datasets. And compared with the runner-up, it has 2.2% and 0.9% higher accuracy in VID I and VID II, respectively.

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Section: A Visibility Estimation Methods Based On Physical Modelmentioning

confidence: 99%

FGS-Net: A Visibility Estimation Method Based on Statistical Feature Stream in Fog Area

Liu

Zhong

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…He [15] refined the value of an image's transmittance by adding a guided filtering technique to improve visibility estimation accuracy. For a better estimate of visibility, Zou [16] proposed a camera calibration-based visibility estimation method that uses nonlinear least squares fitting to obtain the relationship between the eigenvalues of the image and the actual distance. However, due to its strict requirements on the scene and numerous necessary auxiliary parameters, that method is difficult to apply in the complex scene.…”

Section: A Visibility Estimation Methods Based On Physical Modelmentioning

confidence: 99%

STCN-Net: A Novel Multi-Feature Stream Fusion Visibility Estimation Approach

Liu¹,

Chang

Li³

et al. 2022

IEEE Access

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Low visibility always leads to serious traffic accidents worldwide, although extensive works are studied to deal with the estimation of visibility in meteorology areas, it is still a tough problem. Deep learning-based visibility estimation methods, it has low accuracy due to lacking "specific features" of the foggy images. Meanwhile, physical modelbased visibility estimation methods are only applicable to some specific scenes due to its high requirements for extra auxiliary parameters. Therefore, This paper proposes a novel end-to-end framework named STCN-Net for visibility estimation, which combined the "engineered features" and "learned features" to achieve higher accuracy. Specifically, a novel 3D multi-feature stream Matrix, named DDT, is designed for visibility estimation, which is consisted of a transmittance matrix, a dark channel matrix, and a depth matrix. Unlike traditional deep learning methods which only use convolutional neural networks (CNN) to deal with the input data or images, our method combines CNN and Transformer to process the input data or images. In STCN-Net, Swin-Transformer(Swin-T) module takes the original image as input while the CNN module takes the DDT matrix as input. Moreover, in order to integrate different feature information from the CNN and Swin-T, we embed a Coordinate Attention (CA) module in STCN-Net. Finally, two visibility datasets: Visibility Image Dataset Ⅰ (VID I) and Visibility Image Dataset Ⅱ (VID II) were constructed for evaluation where VID I is a real scene visibility dataset and VID II is a synthetic visibility dataset. The experimental results show that our method has better performance than classical methods on the two datasets. And compared with the runner-up, it has 2.1% more accuracy in VID I and 0.5% in VID II.

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“…Atmospheric visibility can also be directly estimated without using Koschmieder's theory. The edge information of the images is utilized to estimate the visibility with a regression model [19][20][21][22][23] or the predefined target location [7,24]. However, these two methods have not been widely adopted due to the absence of a universal regression model for the former method and the low precision of the latter method.…”

Section: Introductionmentioning

confidence: 99%

Error Analysis and Visibility Classification of Camera-Based Visiometer Using SVM under Nonstandard Conditions

Chen

Wang

et al. 2023

Atmosphere

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A camera-based visiometer is a promising atmospheric visibility measurement tool because it can meet some specific demands such as the need for visibility monitoring in a strong way, whereas traditional instruments, such as forward scatter-type sensors and transmissometers, can hardly be widely utilized due to their high cost. The camera-based method is used to measure visibility by recording the luminance contrast of the objects in an image. However, lacking standard conditions, they can hardly obtain absolute measurements even with blackbody objects. In this paper, the errors caused by nonstandard conditions in camera-based visiometers with two artificial black bodies are analyzed. The results show that the luminance contrasts of the two blackbodies are highly dependent on the environmental radiance distribution. The nonuniform sky illuminance can cause a large error in the blackbody contrast estimations, leading to substantial visibility measurement errors. A method based on a support vector machine (SVM) is proposed to classify the visibility under nonstandard conditions to ensure the reliability of the camera-based visiometer. A classification accuracy of 96.77% was achieved for the data containing images depicting different illumination conditions (e.g., a clear sky, cloudy sky, and overcast). The results show that the classifier based on the SVM is an effective and reliable method to estimate visibility under complex conditions.

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Visibility Detection Method Based on Camera Model Calibration

Cited by 6 publications

References 5 publications

FGS-Net: A Visibility Estimation Method Based on Statistical Feature Stream in Fog Area

FGS-Net: A Visibility Estimation Method Based on Statistical Feature Stream in Fog Area

STCN-Net: A Novel Multi-Feature Stream Fusion Visibility Estimation Approach

Error Analysis and Visibility Classification of Camera-Based Visiometer Using SVM under Nonstandard Conditions

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