Computer Vision for Pose Estimation in Real Time
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-3039-5_12
Reference9 articles.
1. Siddiqui, M., & Medioni, G. (2010). Human pose estimation from a single view point, real-time range sensor. IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, 2010, 1–8. https://doi.org/10.1109/CVPRW.2010.5543618
2. Changotra, R., et al. (2022). A comparative study of pose estimation. In 2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM) (pp. 210–216). https://doi.org/10.1109/ICIPTM54933.2022.9754071
3. Bakken, R. H., & Eliassen, L. M. (2012). Real-time 3D skeletonization in computer vision-based human pose estimation using GPGPU. In 2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA) (pp. 61–67). https://doi.org/10.1109/IPTA.2012.6469538
4. Insafutdinov, E., Pishchulin, L., Andres, B., Andriluka, M., & Schiele, B. (2016). DeeperCut: A deeper, stronger, and faster multiperson pose estimation model. In B. Leibe, J. Matas, N. Sebe, & M. Welling (Eds.), Computer vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science (Vol. 9910). Springer. https://doi.org/10.1007/978-3-319-46466-4_3
5. Toshev, A., & Szegedy, C. (2014). DeepPose: Human pose estimation via deep neural networks. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 23–28 June 2014 (pp. 1653–1660).
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