Real-time Object Detection by Road Plane Segmentation Technique for ADAS

摘要

Advanced Driver Assistance Systems (ADAS) are used for assisting the drivers by providing advice and warnings when necessary. CTA (Cross Traffic Alert) systems are a subset of ADAS used for detecting objects (viz., cars, trucks, pedestrians, static objects etc) by using one or more moving cameras, mounted on a vehicle. Usually, CTA systems can detect moving objects within region of interest (ROI). These systems have limitations in detecting static objects present in the RoI and often they fail to detect the objects in shadow regions. Moreover, such systems sometimes detect shadows as the objects. This paper presents a histogram back-projection based road-plane segmentation technique. Histogram back-projections' are applied on saturation and value channels of the video, to detect moving and non moving objects in the ROI. Robustness to the shadow is achieved by applying a logical operation on the back-projections of the saturation and the value channels of the video. Effectiveness of the technique is evaluated by applying the technique on several videos, captured under different scenarios, and by measuring true negatives and false positives for the objects. The technique is suitable for real time applications and can be employed in automatic back-up assistance during the host vehicle parking, blind spot detection, pedestrian detection, and other camera applications for the detection of the objects.