Abstract
In this work, we propose a new approach for 3D human pose estimation from a single monocular RGB image based on a deep convolutional neural network (CNN). The proposed method depends on reducing the huge search space of the continuous-valued 3D human poses by discretizing and approximating these continuous poses into many discrete key-poses. These key-poses constitute more restricted search space and then can be considered as multiple-class candidates of 3D human poses.
Thus, a suitable classification technique is trained using a set of 3D key-poses and their corresponding RGB images to build a model to predict the 3D pose class of an input monocular RGB image. We use deep CNN as a suitable classifier because it is proven to be the most accurate technique for RGB image classification. Our approach is proven to achieve good accuracy which is comparable to the state-of-the-art methods.
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References
Li, S., Zhang, W., Chan, A.B.: Maximum-margin structured learning with deep networks for 3d human pose estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2848–2856 (2015)
Li, S., Chan, Antoni B.: 3D human pose estimation from monocular images with deep convolutional neural network. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9004, pp. 332–347. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16808-1_23
Tekin, B., Katircioglu, I., Salzmann, M., Lepetit, V., Fua, P.: Structured prediction of 3d human pose with deep neural networks. In: Proceedings of the British Machine Vision Conference (BMVC), pp. 130.1–130.11, September 2016
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_53
Yasin, H., Iqbal, U., Kruger, B., Weber, A., Gall, J.: A dual-source approach for 3d pose estimation from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4948–4956 (2016)
Park, S., Hwang, J., Kwak, N.: 3D human pose estimation using convolutional neural networks with 2D pose information. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9915, pp. 156–169. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49409-8_15
Chen, C.H., Ramanan, D.: 3d human pose estimation = 2d pose estimation + matching. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5759–5767, July 2017
Moreno-Noguer, F.: 3d human pose estimation from a single image via distance matrix regression. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1561–1570, July 2017
Tome, D., Russell, C., Agapito, L.: Lifting from the deep: convolutional 3d pose estimation from a single image. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5689–5698, July 2017
Rogez, G., Weinzaepfel, P., Schmid, C.: LCR-Net: localization-classification-regression for human pose. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1216–1224, July 2017
Pavlakos, G., Zhou, X., Derpanis, K.G., Daniilidis, K.: Coarse-to-Fine volumetric prediction for single-image 3d human pose. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1263–1272, July 2017
Kaufman, L., Rousseeuw, P.: Clustering by Means of Medoids. North-Holland, Amsterdam (1987)
Ionescu, C., Papava, D., Olaru, V., Sminchisescu, C.: Human3.6m: Large scale datasets and predictive methods for 3d human sensing in natural environments. IEEE Trans. Pattern Anal. Mach. Intell. 36(7), 1325–1339 (2014)
Thorndike, R.L.: Who belongs in the family? Psychometrika 18(4), 267–276 (1953)
Tekin, B., Rozantsev, A., Lepetit, V., Fua, P.: Direct prediction of 3d body poses from motion compensated sequences. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 991–1000 (2016)
Zhou, X., Zhu, M., Leonardos, S., Derpanis, K.G., Daniilidis, K.: Sparseness meets deepness: 3d human pose estimation from monocular video. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4966–4975 (2016)
Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 675–678. ACM (2014)
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Acknowledgments
The corresponding author would like to thank Intelligent Dynamics Representation Laboratory (Prof. Ogata’s Laboratory), School of Fundamental Science and Engineering, Waseda University, Japan for providing technical support for this research work.
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Elbasiony, R., Gomaa, W., Ogata, T. (2018). Deep 3D Pose Dictionary: 3D Human Pose Estimation from Single RGB Image Using Deep Convolutional Neural Network. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11141. Springer, Cham. https://doi.org/10.1007/978-3-030-01424-7_31
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