Vehicle Distance Estimation from a Monocular Camera for Advanced Driver Assistance Systems

Abstract

The purpose of this study is to propose a framework for accurate and efficient vehicle distance estimation from a monocular camera. The proposed framework consists of a transformer-based object detector, a transformer-based depth estimator, and a distance predictor. The object detector detects various objects that are mostly symmetrical from an image captured by the monocular camera and provides the type of each object and the coordinate information of a bounding box around each object. The depth estimator generates a depth map for the image. Then, the bounding boxes are overlapped with the depth map to extract the depth features of each object, such as the mean depth, minimum depth, and maximum depth of each object. The present study then trained three models—eXtreme Gradient Boosting, Random Forest, and Long Short-Term Memory—to predict the actual distance between the object and the camera based on the type of the object, the bounding box of the object (including its coordinates and size), and the extracted depth features. The present study proposes including the trimmed mean depth of an object to predict the actual distance by excluding the background pixels around an object but within the bounding box of the object. The evaluation results show that the proposed framework outperformed existing studies.