A Neural network approach to visibility range estimation under foggy weather conditions

© 2017 The Authors. Published by Elsevier B.V. The degradation of visibility due to foggy weather conditions is a common trigger for road accidents and, as a result, there has been a growing interest to develop intelligent fog detection and visibility range estimation systems. In this contribution, we provide a brief overview of the state-of-the-art contributions in relation to estimating visibility distance under foggy weather conditions. We then present a neural network approach for estimating visibility distances using a camera that can be fixed to a roadside unit (RSU) or mounted onboard a moving vehicle. We evaluate the proposed solution using a diverse set of images under various fog density scenarios. Our approach shows very promising results that outperform the classical method of estimating the maximum distance at which a selected target can be seen. The originality of the approach stems from the usage of a single camera and a neural network learning phase based on a hybrid global feature descriptor. The proposed method can be applied to support next-generation cooperative hazard & incident warning systems based on I2V, I2I and V2V communications. Peer-review under responsibility of the Conference Program Chairs

Estimating meteorological visibility range under foggy weather conditions: A deep learning approach

© 2018 The Authors. Published by Elsevier Ltd. Systems capable of estimating visibility distances under foggy weather conditions are extremely useful for next-generation cooperative situational awareness and collision avoidance systems. In this paper, we present a brief review of noticeable approaches for determining visibility distance under foggy weather conditions. We then propose a novel approach based on the combination of a deep learning method for feature extraction and an SVM classifier. We present a quantitative evaluation of the proposed solution and show that our approach provides better performance results compared to an earlier approach that was based on the combination of an ANN model and a set of global feature descriptors. Our experimental results show that the proposed solution presents very promising results in support for next-generation situational awareness and cooperative collision avoidance systems. Hence it can potentially contribute towards safer driving conditions in the presence of fog

Camera tamper detection using wavelet analysis for video surveillance

It is generally accepted that video surveillance system operators lose their concentration after a short period of time and may miss important events taking place. In addition, many surveillance systems are frequently left unattended. Because of these reasons, automated analysis of the live video feed and automatic detection of suspicious activity have recently gained importance. To prevent capture of their images, criminals resort to several techniques such as deliberately obscuring the camera view, covering the lens with a foreign object, spraying or defocusing the camera lens. In this paper, we propose some computationally efficient wavelet domain methods for rapid camera tamper detection and identify some real-life problems and propose solutions to these. © 2007 IEEE

Daytime visibility range monitoring through use of a roadside camera

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Daytime visibility range monitoring through use of a roadside camera

Based on a road meteorology standard, we present a roadside camera-based system able to detect daytime fog and to estimate the visibility range. Two detection algorithms, both based on a daytime fog model, are presented along with a process to combine their outputs. Unlike previous methods, the system takes into account the 3-D scene structure and filters the moving objects from the region of interest through use of a background modelling approach and detects the cause of the visibility reduction. The study of the system accuracy with respect to the camera characteristics leads to a specification of the characteristics of the camera required for the system. Some results obtained using a reduced-scale prototyping of the system are presented. Finally, an outlook to future works is given

Visibility And Confidence Estimation Of An Onboard-Camera Image For An Intelligent Vehicle

More and more drivers nowadays enjoy the convenience brought by advanced driver assistances system (ADAS) including collision detection, lane keeping and ACC. However, many assistant functions are still constrained by weather and terrain. In the way towards automated driving, the need of an automatic condition detector is inevitable, since many solutions only work for certain conditions. When it comes to camera, which is most commonly used tool in lane detection, obstacle detection, visibility estimation is one of such important parameters we need to analyze. Although many papers have proposed their own ways to estimate visibility range, there is little research on the question of how to estimate the confidence of an image. In this thesis, we introduce a new way to detect visual distance based on a monocular camera, and thereby we calculate the overall image confidence. Much progresses has been achieved in the past ten years from restoration of foggy images, real-time fog detection to weather classification. However, each method has its own drawbacks, ranging from complexity, cost, and inaccuracy. According to these considerations, the new way we proposed to estimate visibility range is based on a single vision system. In addition, this method can maintain a relatively robust estimation and produce a more accurate result

Measurement of local contrast in images, Application to the measurement of visibility distance through use of an onboard camera

Starting from the definition of the meteorological visibility distance, we define the mobilized and mobilizable visibility distances. This leads to propose a generic method for estimating the atmospheric visibility distance using a camera mounted onboard a moving vehicle. It consists in estimating the most distant object on the road surface having at least a contrast of 5%. In this article, we detail how the contrast is estimated. With this intention, a precise, robust and fast method inspired from the binarization technique of Köhler is presented. We present how this technique was adapted to our needs. To justify our approach, our technique is compared with those of Gordon and Beghdadi. We apply the resulting technique to the measurement of visibility distance by merging our measurement of local contrast with a distance information obtained by stereovision. We finish by giving some examples of measurement of mobilized visibility distance under various meteorological conditions.À partir de la définition de la distance de visibilité météorologique, nous définissons les distances de visibilité mobilisée et mobilisable. Cela nous conduit à proposer une méthode générique de mesure de la distance de visibilité atmosphérique par caméra embarquée à bord d'un véhicule. Celle-ci consiste à rechercher l'objet le plus éloigné ayant un contraste d'au moins 5 %. Nous détaillons dans cet article comment estimer le contraste. Pour ce faire, nous présentons une méthode précise, robuste et rapide issue de la technique de segmentation d'images de Köhler. Nous montrons comment nous avons adapté cette méthode à nos besoins. Pour justifier nos propos, nous comparons notre approche aux techniques de Gordon et Beghdadi. Nous appliquons le résultat à la mesure de distance de visibilité en fusionnant notre mesure de contraste local avec une information de distance obtenue par stéréovision. Nous finissons par donner des exemples de mesure de distance de visibilité mobilisée sous différentes conditions météorologiques