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This paper presents a vision based pedestrian detection system. The presented algorithm is a novel method that accurately segments the pedestrian regions in real time. The fact that the pedestrians are always vertically aligned is taken into consideration. As a result, the edge image is scanned from bottom to top and left to right. Both the color and edge data is combined in order to form the segments. The segmentation is highly dependent on the edge map. Even a single pixel dis-connectivity would lead to incorrect segments. To improve this, a novel edge linking method is performed prior to segmentation. The segmentation would consist of foreground and background segments as well. The background clutter is removed based on certain predefined conditions governed by the camera features. A novel edge based head detection method is proposed for increasing the probability of pedestrian detection. The combination of head and leg pattern will determine the presence of pedestrians.

This paper presents a simple yet novel approach to remove redundant data from outdoor scenes, thus finding significant application in Advanced Driver Assistance Systems (ADAS). A captured outdoor scene has two main parts, the ground region consisting of the road area along with other lane markings and the background region consisting of various structures, trees, sky etc. To extract the ground region, first the yellow and white road markings are segmented based on the HSI (Hue Saturation Intensity) color model and these regions are filled with the surrounding road color. Further the background region is segmented based on the Lab (Color-opponent) color model, which shows significant improvement as compared to other color spaces. To extract the background region such as the sky or ground region, it is assumed that the top and bottom most portions of the image does not consist of useful information.