Automatic Labeled LiDAR Data Generation and Distance-Based Ensemble Learning for Human Segmentation

Abstract

Following the improvements in deep neural networks, state-of-the-art networks have been proposed for human segmentation using point clouds captured by light detection and ranging. However, the performance of these networks depends significantly on the training data. An issue with collecting training data is labeling. Labeling by humans is necessary to obtain ground-truth labels; however, labeling involves high costs. Therefore, we propose an automatically labeled data generation pipeline, for which we can change any parameters or data generation environments. Our approach uses a human model named Dhaiba and the background of Miraikan to generate realistic artificial data. We present 1M data generated by the proposed pipeline. Furthermore, we propose an ensemble learning based on generated data for utilizing our data generation pipeline. This paper proposes the specifications of the pipeline, data details, and explanation of ensemble learning with evaluations of various approaches.

Learning-Based Human Segmentation and Velocity Estimation Using Automatic Labeled LiDAR Sequence for Training

“…Grand Theft Auto is not designed for research purposes; therefore, we cannot control specific properties of the circumstances of the simulation, such as the human body type and model deployment location. Automatic Labeled LiDAR Data [11], [12] have been released with 1M+ data including depth, xyz coordinates and pixel-wise human labels. Automatic Labeled LiDAR Data may cover demands in the frame domain; however, its application is not sufficient to address the requirements of the sequence domain.…”

“…5-(b) and (c) are inspired by Fully Convolutional Network (FCN) [3]. FCN shows effectiveness for image segmentation [3] and is also known to be valid for LiDAR data [11], [12]. However, since the FCN only treats a single frame as input, we expanded the FCN architecture to feature extraction network and decoding network for handling multiple frames efficiently.…”

“…In recent study [12], ensemble network was proposed and evaluated using same real data. Therefore, the proposed network was compared with the results reported in [12] as shown in Table 7. We can confirm that the proposed network shows significantly better score than other networks.…”

“…Human recognition with LiDAR data has been researched vigorously [5], [11], [12]. According to [12], the human recognition performance drastically decreases as distance between a human and LiDAR increases.…”

“…Human recognition with LiDAR data has been researched vigorously [5], [11], [12]. According to [12], the human recognition performance drastically decreases as distance between a human and LiDAR increases. Because the number of points is inversely proportional to the square of the distance between a human and LiDAR, humans in the distance are difficult to recognize only with the very sparse LiDAR data.…”