Deep Pedestrian Distance Estimation from Single-shot Image
Kazuki Murayama, Kenji Kanai, Masaru Takeuchi, Jiro Katto · 2020
In this paper, we propose a deep learning-based distance estimation method from a single-shot image. In the proposal, we model the estimation as a regression problem, and estimate the distance between a pedestrian and a camera by using three main features; size of bounding box, image blur and image features. By using KITTI dataset, we evaluate the accuracy of the proposed model.