An Efficient Semi-Automated Scheme for Infrastructure LiDAR Annotation

Aotian Wu, Pan He, Xiao Li, Sanjay Ranka, Anand Rangarajan

Abstract:

—Most existing perception systems rely on sensory
data acquired from cameras, which perform poorly in low light
and adverse weather conditions. To resolve this limitation, we
have witnessed advanced LiDAR sensors become popular in perception tasks in autonomous driving applications. Nevertheless,
their usage in traffic monitoring systems is less ubiquitous. We
identify two significant obstacles in cost-effectively and efficiently
developing such a LiDAR-based traffic monitoring system: (i)
public LiDAR datasets are insufficient for supporting perception
tasks in infrastructure systems, and (ii) 3D annotations on
LiDAR point clouds are time-consuming and expensive. To fill
this gap, we present an efficient semi-automated annotation tool
that automatically annotates LiDAR sequences with tracking
algorithms while offering a fully annotated infrastructure LiDAR
dataset—FLORIDA (Florida LiDAR-based Object Recognition
and Intelligent Data Annotation)—which will be made publicly
available. Our advanced annotation tool seamlessly integrates
multi-object tracking (MOT), single-object tracking (SOT), and
suitable trajectory post-processing techniques. Specifically, we
introduce a human-in-the-loop schema in which annotators
recursively fix and refine annotations imperfectly predicted by
our tool and incrementally add them to the training dataset to
obtain better SOT and MOT models. By repeating the process,
we significantly increase the overall annotation speed by 3 − 4
times and obtain better qualitative annotations than a stateof-the-art annotation tool. The human annotation experiments
verify the effectiveness of our annotation tool. In addition, we
provide detailed statistics and object detection evaluation results
for our dataset in serving as a benchmark for perception tasks
at traffic intersections.