  In this paper, we propose an automatic labeled sequential data generation
pipeline for human segmentation and velocity estimation with point clouds.
Considering the impact of deep neural networks, state-of-the-art network
architectures have been proposed for human recognition using point clouds
captured by Light Detection and Ranging (LiDAR). However, one disadvantage is
that legacy datasets may only cover the image domain without providing
important label information and this limitation has disturbed the progress of
research to date. Therefore, we develop an automatic labeled sequential data
generation pipeline, in which we can control any parameter or data generation
environment with pixel-wise and per-frame ground truth segmentation and
pixel-wise velocity information for human recognition. Our approach uses a
precise human model and reproduces a precise motion to generate realistic
artificial data. We present more than 7K video sequences which consist of 32
frames generated by the proposed pipeline. With the proposed sequence
generator, we confirm that human segmentation performance is improved when
using the video domain compared to when using the image domain. We also
evaluate our data by comparing with data generated under different conditions.
In addition, we estimate pedestrian velocity with LiDAR by only utilizing data
generated by the proposed pipeline.
