  Smart monitoring using three-dimensional (3D) image sensors has been
attracting attention in the context of smart cities. In smart monitoring,
object detection from point cloud data acquired by 3D image sensors is
implemented for detecting moving objects such as vehicles and pedestrians to
ensure safety on the road. However, the features of point cloud data are
diversified due to the characteristics of light detection and ranging (LIDAR)
units used as 3D image sensors or the install position of the 3D image sensors.
Although a variety of deep learning (DL) models for object detection from point
cloud data have been studied to date, no research has considered how to use
multiple DL models in accordance with the features of the point cloud data. In
this work, we propose a feature-based model selection framework that creates
various DL models by using multiple DL methods and by utilizing training data
with pseudo incompleteness generated by two artificial techniques: sampling and
noise adding. It selects the most suitable DL model for the object detection
task in accordance with the features of the point cloud data acquired in the
real environment. To demonstrate the effectiveness of the proposed framework,
we compare the performance of multiple DL models using benchmark datasets
created from the KITTI dataset and present example results of object detection
obtained through a real outdoor experiment. Depending on the situation, the
detection accuracy varies up to 32% between DL models, which confirms the
importance of selecting an appropriate DL model according to the situation.
