A regional distance regression network for monocular object distance estimation

摘要

Monocular pipelines are convenient and cost-effective solutions for object distance estimation in 3D vision. Current methods for monocular object distance estimation either perform inaccurately or require heavy work on data collection. In this paper, we propose a network with R-CNN based structure to implement object detection and distance estimation simultaneously. We append an efficient branch to integrate the information of camera extrinsic parameters with RGB data in our network. Further, optimized multi-scale feature is utilized to enrich the representation power of deep feature, hence to enhance the estimation accuracy. Finally, several regression methods are explored to improve distance estimation results. We train and validate our network on KITTI object dataset, and compare with other methods to show that our method is accurate and easy to train. To prove the generality of our method under other scenarios, we construct a dataset of surveillance scenes, and conduct similar experiments on this dataset.