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Advanced Driver Assistance System (ADAS) in combination with other active safety features like air bags etc. is gaining popularity. Vision based ADAS systems perform well under ideal lighting, illumination and environmental conditions. However, with change in illumination and other lighting related factors, the effectiveness of vision based ADAS systems tend to deteriorate. Under conditions of low light, it is therefore important to develop techniques that would offset the effects of low illumination and generate an image that appears as if it were taken under ideal lighting conditions. To accomplish this, we have developed a method, that uses local color statistics from the host image with low illumination, and enhance the same using an adaptive color transfer mechanism. By taking cues from the properties of ideal images that are saved in a database, the proposed method tends to recreate the input scene (with low illumination), into a near ideal scene, based on the database images.

Various Advanced Driver Assists Systems (ADAS) are being used today to increase safety of drivers. These systems viz. Forward Collision Warning (FCW), Lane Departure Warning (LDW), Pedestrian Detection (PD), are all based on inputs captured using a front mounted camera. It would be useful to combine all these applications together and process the same input for different application purpose. Additionally, multicore processors are now easily available and can be used for integrating multiple ADAS applications. This would lead to reduced cost and maintenance of ADAS systems with the same performance benefits. Since current ADAS applications are sequential and/or use single core processors there is a need to parallelize these applications so that multiple cores can be utilized optimally. In this paper, we discuss our experiments and results while attempting to integrate two such ADAS applications on a multicore embedded platform.