  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 three to four times and obtain better
qualitative annotations than a state-of-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.
