The usage of convolutional neural networks (CNNs)

No abstract

Following improvements in deep neural networks, stateof-the-art netwy depends on the training data. An issue with collecting training data is labeling. Labeling by humans is necessary to obtain the ground truth label; however, labeling requires huge costs. Therefore, we propose an automatic labeled data generation pipeline, for which we can change any parameters or data generation environments. Our approach uses a human model named Dhaiba and a background of Miraikan and consequently generated realistic artificial data. We present 500k+ data generated by the proposed pipeline. This paper also describes the specification oforks have been proposed for human recog-nition using point clouds captured by LiDAR. However, the performance of these networks strongl the pipeline and data details with evaluations of various approaches.