  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.
