Abstract
Generally, multi-person pose estimation plays a crucial role in behavior recognition in images and videos. Previously, pose estimation of a single person is popular and achieves high prediction accuracy with the development of deep learning. However, pose estimation of multi-person remains to be a huge challenge and cannot achieve the same effect as that of a single person. It mainly results from the rare, missing or incorrect location detection and overlap of pose, which are usually caused by incomplete person identification. Therefore, we propose an improved two-stage multi-person pose estimation model (ITMPE) to further improve the performance of multi-person pose estimation. The first stage, Mask R-CNN is used for person identification. The second stage, processed images or videos with identified people only are fed into OpenPose model for multi-person pose estimation. The comparative experiments show that our proposed model achieves a significant improvement than original model. Our proposed model reduces the MSE, MAE by around 27.38%, 21.57% and increases R2, Mean values by 49.80% and 96.91% on average, respectively. The improvement in person identification and misclassification are shown in our comparison images. More people are captured and given the pose estimation, which directly affect the performance of behavior recognition.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cao, Z., Simon, T., Wei, S.E., Sheikh, Y.: Realtime multi-person 2D pose estimation using part affinity fields. In: Proceedings of 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 (2017)
Zhu, X., Jiang, Y., Luo, Z.: Multi-person pose estimation for PoseTrack with enhanced part affinity fields. In: ICCVW (2017)
He, K., Gkioxari, G., Dollar, P., Girshick, R.: Mask R-CNN. In: Proceedings of IEEE International Conference on Computational Vision (2017)
Yang, Y., Ramanan, D.: Articulated human detection with flexible mixtures of parts. TPAMI 35, 2878–2890 (2013)
Dantone, M., Gall, J., Leistner, C., Van Gool, L.: Human pose estimation using body parts dependent joint regressors. In: CVPR (2013)
Chu, X., Yang, W., Ouyang, W., Ma, C., Yuille, A.L., Wang, X.: Multi-context attention for human pose estimation. In: CVPR (2017)
Tang, W., Yu, P., Wu, Y.: Deeply learned compositional models for human pose estimation. In: ECCV (2018)
Girshick, R.: Fast R-CNN. In: Proceedings of IEEE International Conference on Computer Vision (2015). https://doi.org/10.1109/iccv.2015.169
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 (2017)
Simonyan, K., Zisserman, A.: VGG-16. arXiv Preprint (2014)
Wei, S.-E., Ramakrishna, V., Kanade, T., Sheikh, Y.: Convolutional pose machines. In: CVPR (2016)
Chen, K., Loy, C.C., Gong, S., Xiang, T.: Feature Mining for Localised Crowd Counting (2012)
Idrees, H., Saleemi, I., Seibert, C., Shah, M.: Multi-source multi-scale counting in extremely dense crowd images. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2013)
Zhang, Y., Zhou, D., Chen, S., Gao, S., Ma, Y.: Single-image crowd counting via multi-column convolutional neural network. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2016)
Panteleris, P., Oikonomidis, I., Argyros, A.: Using a single RGB frame for real time 3D hand pose estimation in the wild. In: Proceedings of 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018 (2018)
Li, M., Zhou, Z., Liu, X.: Multi-person pose estimation using bounding box constraint and LSTM. IEEE Trans. Multimedia (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, S., Wang, Y., Wang, X., Ye, X., Li, H., Chen, X. (2019). An Improved Two-Stage Multi-person Pose Estimation Model. In: Chen, J., Huynh, V., Nguyen, GN., Tang, X. (eds) Knowledge and Systems Sciences. KSS 2019. Communications in Computer and Information Science, vol 1103. Springer, Singapore. https://doi.org/10.1007/978-981-15-1209-4_2
Download citation
DOI: https://doi.org/10.1007/978-981-15-1209-4_2
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1208-7
Online ISBN: 978-981-15-1209-4
eBook Packages: Computer ScienceComputer Science (R0)