Advanced driver assistance systems (ADAS) have achieved a compelling success. However, existing methods met some inevitable drawbacks in security and cost. For instance, deep-learning-based detection algorithm are unstable in real-world applications, and the hardware configurations are too expensive to be deployed on most of vehicles. To address these issues, we build a novel ADAS, termed as STAR EYES. We combine conventional computer vision technology and deep-learning-based methods to build more precise and steady algorithms, meanwhile develop the algorithm of monocular ranging distance with assistance of only cameras. Besides, in order to help drivers, we first add a panoramic stitching and 3D live road model into ADAS products for comfortable feelings.

Avoid hazards: Lane Detection, Object Recognization and Ranging

Lane detection can help us to concentrate on objects in the same road with driver. The most familiar approach to fit line is proposed. In this method, Hough transform and least square have been combined to process experiment data and the contour of binary images.

YOLOv3 is a real-time object detection algorithm that identifies specific objects in videos, live feeds, or images. YOLO uses features learned by a deep convolutional neural network to detect an object.

In the case of real-time driving, YOLOv3 has better performance that the other models in reaction and accuracy.

Monocular camera ranging is really a huge challenge to everyone engaged in computer vision. To avoid the deviation caused by the different parameters of cameras, the best way is to recaculate the Distortion Coefficients and Calibration Matrix. The specific process to repeat Affine transformation over and over again, colloquaily, up and down.

Perceive like a sensor: Panoramic stitching and 3D Mapping

By mixing camera pictures from four differnent sides, we could get an aerial view of car and its around. It helps drivers observe around more precisely.

Obviously, large amounts of products can obtain data and handle it with programming like image process or machine learning. In real-time driving, all the objects coordinate was mapping to 3D model in screen. This function builds a succinct picture to express relative positions of every vehicles, thus helping drivers observe the road more precisely.