Abstract
Nowadays Human Pose Estimation (HPE) represents one of the main research themes in the field of computer vision. Despite innovative methods and solutions introduced for frame processing algorithms, the use of standard frame-based cameras still has several drawbacks such as data redundancy and fixed frame-rate. The use of event-based cameras guarantees higher temporal resolution with lower memory and computational cost while preserving the significant information to be processed and thus it represents a new solution for real-time applications. In this paper, the DHP19 dataset was employed, the first and, to date, the only one with HPE data recorded from Dynamic Vision Sensor (DVS) event-based cameras. Starting from the baseline single-input single-output (SISO) Convolutional Neural Network (CNN) model proposed in the literature, a novel multi-input multi-output (MIMO) CNN-based architecture was proposed in order to model simultaneously two different single camera views. Experimental results show that the proposed MIMO approach outperforms the standard SISO model in terms of accuracy and training time.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The code to reproduce all results is available at the following link: https://github.com/AlessandroManilii/3D_HumanPoseEstimation_event-based_dataset.
References
Amin, S., Andriluka, M., Rohrbach, M., Schiele, B.: Multi-view pictorial structures for 3d human pose estimation. In: 24th British Machine Vision Conference, pp. 1–12. BMVA Press (2013)
Amir, A., et al.: A low power, fully event-based gesture recognition system. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7388–7397 (2017)
Brandli, C., Berner, R., Yang, M., Liu, S.C., Delbruck, T.: A 240\(\times \) 180 130 DB 3 \(\mu \)s latency global shutter spatiotemporal vision sensor. IEEE J. Solid-State Circuits 49(10), 2333–2341 (2014)
Calabrese, E., et al.: Dhp19: dynamic vision sensor 3d human pose dataset. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2019
Cao, Z., Simon, T., Wei, S., Sheikh, Y., et al.: Openpose: realtime multi-person 2d pose estimation using part affinity fields. IEEE Trans. Pattern Anal. Mach. Intell. 42(5), 1146-1161 (2019)
Capecci, M., et al.: A tool for home-based rehabilitation allowing for clinical evaluation in a visual markerless scenario. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 8034–8037. IEEE (2015)
Capecci, M., et al.: The kimore dataset: kinematic assessment of movement and clinical scores for remote monitoring of physical rehabilitation. IEEE Trans. Neural Syst. Rehabil. Eng. 27(7), 1436–1448 (2019)
Hu, Y., Liu, H., Pfeiffer, M., Delbruck, T.: DVS benchmark datasets for object tracking, action recognition, and object recognition. Front. Neurosci. 10, 405 (2016). https://doi.org/10.3389/fnins.2016.00405, https://www.frontiersin.org/article/10.3389/fnins.2016.00405
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)
Lichtsteiner, P., Posch, C., Delbruck, T.: A 128\(\times \)128 120 DB 15\(\mu \) s latency asynchronous temporal contrast vision sensor. IEEE J. Solid-State Circuits 43(2), 566–576 (2008)
Liciotti, D., Paolanti, M., Frontoni, E., Mancini, A., Zingaretti, P.: Person re-identification dataset with RGB-D camera in a top-view configuration. In: Nasrollahi, K., Distante, C., Hua, G., Cavallaro, A., Moeslund, T.B., Battiato, S., Ji, Q. (eds.) FFER/VAAM -2016. LNCS, vol. 10165, pp. 1–11. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-56687-0_1
Liu, H., Moeys, D.P., Das, G., Neil, D., Liu, S., Delbrück, T.: Combined frame- and event-based detection and tracking. In: 2016 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 2511–2514 (2016)
Lungu, I., Corradi, F., Delbrück, T.: Live demonstration: convolutional neural network driven by dynamic vision sensor playing roshambo. In: 2017 IEEE International Symposium on Circuits and Systems (ISCAS), p. 1 (2017)
Maqueda, A.I., Loquercio, A., Gallego, G., García, N., Scaramuzza, D.: Event-based vision meets deep learning on steering prediction for self-driving cars. CoRR abs/1804.01310 (2018), http://arxiv.org/abs/1804.01310
Mehta, D., Rhodin, H., Casas, D., Sotnychenko, O., Xu, W., Theobalt, C.: Monocular 3D human pose estimation using transfer learning and improved CNN supervision. CoRR abs/1611.09813 (2016), http://arxiv.org/abs/1611.09813
Moccia, S., Migliorelli, L., Carnielli, V., Frontoni, E.: Preterm infants’ pose estimation with spatio-temporal features. IEEE Trans. Biomed. Eng. 67(8), 2370–2380 (2019)
Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 483–499. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_29
Paolanti, M., Romeo, L., Liciotti, D., Pietrini, R., Cenci, A., Frontoni, E., Zingaretti, P.: Person re-identification with RGB-D camera in top-view configuration through multiple nearest neighbor classifiers and neighborhood component features selection. Sensors 18(10), 3471 (2018)
Paolanti, M., Romeo, L., Martini, M., Mancini, A., Frontoni, E., Zingaretti, P.: Robotic retail surveying by deep learning visual and textual data. Robot. Auton. Syst. 118, 179–188 (2019)
Rhodin, H., Robertini, N., Casas, D., Richardt, C., Seidel, H., Theobalt, C.: General automatic human shape and motion capture using volumetric contour cues. CoRR abs/1607.08659 (2016), http://arxiv.org/abs/1607.08659
Sigal, L., Balan, A., Black, M.J.: HumanEva: synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion. Int. J. Comput. Vision 87(1), 4–27 (2010)
Sun, K., Xiao, B., Liu, D., Wang, J.: Deep high-resolution representation learning for human pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5693–5703 (2019)
Toshev, A., Szegedy, C.: Deeppose: human pose estimation via deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1653–1660 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Manilii, A., Lucarelli, L., Rosati, R., Romeo, L., Mancini, A., Frontoni, E. (2021). 3D Human Pose Estimation Based on Multi-Input Multi-Output Convolutional Neural Network and Event Cameras: A Proof of Concept on the DHP19 Dataset. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12661. Springer, Cham. https://doi.org/10.1007/978-3-030-68763-2_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-68763-2_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-68762-5
Online ISBN: 978-3-030-68763-2
eBook Packages: Computer ScienceComputer Science (R0)