In modern cars, the Advanced Driver Assistance Systems (ADAS) is cardinal point for safety and regulation. The proposed method detects visual saliency region in a given image. Multiple ADAS systems require number of sensors and multicore processors for fast processing of data in real time, which leads to the increase in cost. In order to balance the cost and safety, the system should process only required information and ignore the rest. Human visual system perceives only important content in a scene while leaving rest of portions unprocessed. The proposed method aims to model this behavior of human visual system in computer vision/image processing applications for eliminating non salient objects from an image. A region is said to be salient, if its appearance is unique. In our method, the saliency in still images is computed by local color contrast difference between the regions in Lab space. In addition, the motion information is an important feature which is incorporated with image fixation map to produce final saliency for videos. Experimental results on standard datasets show that the proposed method produces saliency map with well-defined object boundaries. In comparison with other existing state of art methods, the proposed algorithm suppresses the background information efficiently.