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Deep Learning based Human Detection

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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.

Data Access



Single Image


Automatic Labeled LiDAR Data Generation based on Precise Human Model (pdf)

Wonjik Kim , Masayuki Tanaka, Masatoshi Okutomi, Yoko Sasaki
International Conference on Robotics and Automation (ICRA), 2019.

Automatic Labeled LiDAR Data



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

Wonjik Kim , Masayuki Tanaka, Masatoshi Okutomi, Yoko Sasaki
IEEE Access, 2019.

Distance Sperated Automatic Labeled LiDAR Data for Human detection

Publication



Conference paper


Automatic Labeled LiDAR Data Generation based on Precise Human Model (pdf)

Wonjik Kim , Masayuki Tanaka, Masatoshi Okutomi, Yoko Sasaki

International Conference on Robotics and Automation (ICRA), 2019.



Following improvements in deep neural networks, state-of-the-art networks have been proposed for human recog- nition using point clouds captured by LiDAR. However, the performance of these networks strongly 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 the Miraikan and consequently generated re- alistic artificial data. We present 400k+ data generated by the proposed pipeline. This paper also describes the specification of the pipeline and data details with evaluations of various approaches.



Journal paper


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

Wonjik Kim , Masayuki Tanaka, Masatoshi Okutomi, Yoko Sasaki

IEEE Access, 2019.



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.

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