Abstract
Raindrops adhered to a windscreen or window glass can significantly degrade the visibility of a scene. Modeling, detecting and removing raindrops will, therefore, benefit many computer vision applications, particularly outdoor surveillance systems and intelligent vehicle systems. In this paper, a method that automatically detects and removes adherent raindrops is introduced. The core idea is to exploit the local spatio-temporal derivatives of raindrops. To accomplish the idea, we first model adherent raindrops using law of physics, and detect raindrops based on these models in combination with motion and intensity temporal derivatives of the input video. Having detected the raindrops, we remove them and restore the images based on an analysis that some areas of raindrops completely occludes the scene, and some other areas occlude only partially. For partially occluding areas, we restore them by retrieving as much as possible information of the scene, namely, by solving a blending function on the detected partially occluding areas using the temporal intensity derivative. For completely occluding areas, we recover them by using a video completion technique. Experimental results using various real videos show the effectiveness of our method.
Highlights
OUTDOOR vision systems employed for various tasks such as navigation, data collection and surveillance, can be adversely affected by bad weather conditions such as rain, haze and snow
We restore them by retrieving as much as possible information of the scene, and for completely occluding areas, we recover them by using a video completion technique
We create a real data by dropping water on a transparent panel as the ground truth and combining intensity change feature (IC) with OF, we obtain the best performance to detect all of the raindrops while keeping a low false alarm rate
Summary
OUTDOOR vision systems employed for various tasks such as navigation, data collection and surveillance, can be adversely affected by bad weather conditions such as rain, haze and snow. Raindrops inevitably adhered to windscreens, camera lenses, or protecting shields These adherent raindrops occlude and deform some image areas, causing the performances of many algorithms in the vision systems such as feature detection, tracking, stereo correspondence, etc., to be significantly degraded. This problem occurs for vision systems that use a hand-held camera or a top-mounted vehicle sensor where no wipers can be used. As for raindrop removal, Roser and Geiger [14] address it using image registration, and Yamashita et al [18], [19] utilize position and motion constraints from specific cameras. While our method achieves optimal accuracy when using both the intensitychange and motion based features, only intensity-change based feature can work in real time
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