SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation

TL;DR

The paper introduces SHLE, a stereo-based pipeline for accurate height limit device detection and depth filtering, improving safety systems for over-height vehicle strike prevention.

Contribution

A novel two-stage stereo-based method with a new device detection scheme and a large-scale dataset for benchmarking height limit estimation.

Findings

Achieves an average error below 10cm at 70m distance

Outperforms all compared baselines

State-of-the-art performance on the benchmark dataset

Abstract

Recently, over-height vehicle strike frequently occurs, causing great economic cost and serious safety problems. Hence, an alert system which can accurately discover any possible height limiting devices in advance is necessary to be employed in modern large or medium sized cars, such as touring cars. Detecting and estimating the height limiting devices act as the key point of a successful height limit alert system. Though there are some works research height limit estimation, existing methods are either too computational expensive or not accurate enough. In this paper, we propose a novel stereo-based pipeline named SHLE for height limit estimation. Our SHLE pipeline consists of two stages. In stage 1, a novel devices detection and tracking scheme is introduced, which accurately locate the height limit devices in the left or right image. Then, in stage 2, the depth is temporally measured, extracted and filtered to calculate the height limit device. To benchmark the height limit estimation task, we build a large-scale dataset named "Disparity Height", where stereo images, pre-computed disparities and ground-truth height limit annotations are provided. We conducted extensive experiments on "Disparity Height" and the results show that SHLE achieves an average error below than 10cm though the car is 70m away from the devices. Our method also outperforms all compared baselines and achieves state-of-the-art performance. Code is available at https://github.com/Yang-Kaixing/SHLE.