Automatic Glare Detection via Photometric, Geometric, and Global Positioning Information

Glare due to sunlight, moonlight, or other light sources can be a serious impediment during autonomous or manual driving. Automatically detecting the presence, location, and severity of such glare can be of critical importance for an autonomous driving system, which may then give greater priority to other sensors or cues/parts of the scene. We present an algorithm for automatic real-time glare detection that uses a combination of: (1) the intensity, saturation, and local contrast of the input frame; (2) shape detection; and (3) solar azimuth and elevation computed based on the position and heading information from the GPS (used under daylight conditions). These data are used to generate a glare occurrence map that indicates the center location(s) and extent(s) of the glare region(s). Testing on a variety of daytime and nighttime scenes demonstrates that the proposed system is effective at glare detection and is capable of real-time operation. Introduction A very common occurrence during navigation is the presence of overly intense light that can overwhelm the driver or visual sensors of an autonomous ground vehicle, and thereby reduce visibility. When dazzled by a light source, typical reactions by the drivers are (1) flipping down the sunshades, which is not always effective, especially when the glare is caused by a low-positioned external light source, e.g., low sun or direct light from the headlights of the oncoming vehicle; (2) using one hand to occlude the strong light source from the eyes of the driver, which is not safe and can block the view of some important obstacles on the road; and (3) wearing sunglasses, which has comfort issues [1]. For autonomous vehicles which depend on onboard cameras for navigation, none of these reactions are possible, thus resulting in the partial or full loss of environment perception and subsequent failures. Although the detection of glare is an important problem, there are relatively few publications on glare detection using digital cameras [2, 3, 4, 5, 1, 6]. The existing approaches to glare detection generally rely on one or two simplistic image properties, followed by ad-hoc thresholding. The obvious and most commonly used feature is light intensity [2, 3], which can certainly detect the light source(s), but which cannot differentiate these sources from other bright regions (e.g., clouds), as demonstrated in Figure 1. To overcome this limitation, other approaches supplement intensity with hue, followed by spatial filtering or transformations [4, 5]. However, hue often fails to delineate the boundaries of the glare region(s), particularly when the image is subject to compression, as demonstrated in Figure 2. Instead of analyzing the scene, another class of approach has focused on analyzing an image of the drivers face [1, 6]. Unfortunately, this approach cannot be used in autonomous systems nor in scenarios where the drivers environment is different from the actual to-be-driven location (e.g., in remote navigation). Figure 1. Using light intensity alone to detect the glare regions also captures bright, non-glare regions: (a) Original image; (b) binarized intensity values using a threshold of 95% of the maximum value. Figure 2. HSV color space components of an image with glare: (a) Intensity values (V channel); (b) hue values (H channel); (c) saturation values (S channel). Previous approaches have used intensity and hue; part of our approach uses intensity and saturation, the latter being more reliable at detecting the spatial extent of each glare region. In this paper, we present an algorithm for glare detection that can reliably detect the presence of glare in both daytime and nighttime settings, and which can be used in autonomous or remote navigation settings (i.e., which does not rely on the analysis and co-presence of a human driver). Our approach employs an adaptive combination of features which remain computational simplistic, but which can reliably detect both the location(s) and extent(s) of the glare region(s). We specifically use a combination of: (1) the intensity, saturation, and local contrast of the input frame; (2) shape detection; and (3) solar azimuth and elevation computed based on the position and heading information from the GPS (used under daylight conditions). These data are used to generate a glare occurrence map that indicates the center location(s) and extent(s) of the glare region(s). The main contributions of this work are as follows: Our approach is the first to use a combination of photometric, geometric, and GPS information to perform glare detection. For the photoIS&T International Symposium on Electronic Imaging 2017 Autonomous Vehicles and Machines 2017 77 https://doi.org/10.2352/ISSN.2470-1173.2017.19.AVM-024 © 2017, Society for Imaging Science and Technology