Vision Algorithms for Rain and Traffic Lights in Driver Assistance Systems

Abstract : Vision algorithms can be used to expand the working range of the assistance systems so as to deal with urban scenes or degraded weathers. To this end, three novel applications are investigated in this thesis for both rain and traffic lights. Rain is the most frequent degraded weather condition. We review the various physics and photometry models for rain and raindrops, and highlight some of the misuses. When driving in daytime the raindrops on the windscreen lower the driver visibility. For standard on-board camera these drops appear as unfocused. Hence, we investigate the detection of unfocused raindrops using blur maps or lack of gradients with photometry. For nightime driving in rain, the headlights paradoxically reduce the visibility due to light reflected off of raindrops back toward the driver. Relying on a physic-based simulator, we propose to build an illumination device that would illuminate the scene without shining the falling particles. The performance of the simulator and a proof-of-concept prototype sustain that our idea can efficiently improve the overall scene visibility. Fast reactive drops detection and tracking is also investigated.To deal with urban scenes, traffic lights play a key role. Though traffic light recognition was attempted in the past, the existing algorithms can't handle complex scenarios. Hence, we have developed a traffic light recognition algorithm that uses a grayscale spot light detection and a template matching classification. Our approach is modular and capable of detecting various kind of traffic lights even when using a low-dynamic camera. We have evaluated our algorithm on sequences from France, China and Switzerland.
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Raoul de Charette. Vision Algorithms for Rain and Traffic Lights in Driver Assistance Systems. Other. Ecole Nationale Supérieure des Mines de Paris, 2012. English. ⟨NNT : 2012ENMP0053⟩. ⟨pastel-00802707⟩

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