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Interact-Pose Datasets for 2D Human Pose Estimation in Multi-person Interaction Scene

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Artificial Intelligence and Robotics (ISAIR 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1701))

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Abstract

In recent years, several excellent works dealing with human pose estimation in complex multi-person scenes have focus on the problems of complex backgrounds and the multiplayer scene. However, when facing the scene of multi-person interaction, the results of current mainstream algorithms are still unsatisfactory and some common datasets are not suitable for coping with interaction problems. Therefore, we propose a new dataset named Interact-Pose for solving multi-person interactions problems. Firstly, We use the MSCOCO format to annotate Interact-Pose. Except that, we adopt the corresponding data augmentation scheme to exchange the background of the Interact-Pose Dataset to make it more complex and have better generalization performance. Then it is trained after being fused with the MSCOCO dataset. After training on HigherHRNet, the average AP value of our test results is 67.3% on the Validation set of COCO2017, which is higher than that of the test only being trained by MSCOCO.

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Authors and Affiliations

  1. Nanjing University of Posts and Communications, Nanjing, China

    Yifei Jiang & Hao Gao

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  1. Yifei Jiang
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  2. Hao Gao
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Correspondence to Hao Gao .

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Editors and Affiliations

  1. Kyushu Institute of Technology, Kitakyushu, Japan

    Shuo Yang

  2. Kyushu Institute of Technology, Kitakyushu, Japan

    Huimin Lu

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Jiang, Y., Gao, H. (2022). Interact-Pose Datasets for 2D Human Pose Estimation in Multi-person Interaction Scene. In: Yang, S., Lu, H. (eds) Artificial Intelligence and Robotics. ISAIR 2022. Communications in Computer and Information Science, vol 1701. Springer, Singapore. https://doi.org/10.1007/978-981-19-7943-9_18

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  • DOI: https://doi.org/10.1007/978-981-19-7943-9_18

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-7942-2

  • Online ISBN: 978-981-19-7943-9

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