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2020 | OriginalPaper | Buchkapitel

Real-Time Recognition of Daily Actions Based on 3D Joint Movements and Fisher Encoding

verfasst von : Panagiotis Giannakeris, Georgios Meditskos, Konstantinos Avgerinakis, Stefanos Vrochidis, Ioannis Kompatsiaris

Erschienen in: MultiMedia Modeling

Verlag: Springer International Publishing

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Abstract

Recognition of daily actions is an essential part of Ambient Assisted Living (AAL) applications and still not fully solved. In this work, we propose a novel framework for the recognition of actions of daily living from depth-videos. The framework is based on low-level human pose movement descriptors extracted from 3D joint trajectories as well as differential values that encode speed and acceleration information. The joints are detected using a depth sensor. The low-level descriptors are then aggregated into discriminative high-level action representations by modeling prototype pose movements with Gaussian Mixtures and then using a Fisher encoding schema. The resulting Fisher vectors are suitable to train Linear SVM classifiers so as to recognize actions in pre-segmented video clips, alleviating the need for additional parameter search with non-linear kernels or neural network tuning. Experimental evaluation on two well-known RGB-D action datasets reveal that the proposed framework achieves close to state-of-the-art performance whilst maintaining high processing speeds.

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Vorheriges Kapitel Instrument Recognition in Laparoscopy for Technical Skill Assessment
Nächstes Kapitel Model-Based and Class-Based Fusion of Multisensor Data
Literatur
1.
Avgerinakis, K., Briassouli, A., Kompatsiaris, Y.: Activity detection using sequential statistical boundary detection (SSBD). Comput. Vis. Image Underst. 144, 46–61 (2016)CrossRef
2.
Chéron, G., Laptev, I., Schmid, C.: P-CNN: pose-based CNN features for action recognition. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3218–3226 (2015)
3.
Devanne, M., Wannous, H., Berretti, S., Pala, P., Daoudi, M., Del Bimbo, A.: 3-D human action recognition by shape analysis of motion trajectories on riemannian manifold. IEEE Trans. Cybern. 45(7), 1340–1352 (2014)CrossRef
4.
Du, W., Wang, Y., Qiao, Y.: RPAN: an end-to-end recurrent pose-attention network for action recognition in videos. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3725–3734 (2017)
5.
Feichtenhofer, C., Pinz, A., Wildes, R.: Spatiotemporal residual networks for video action recognition. In: Advances in Neural Information Processing Systems, pp. 3468–3476 (2016)
6.
Feichtenhofer, C., Pinz, A., Zisserman, A.: Convolutional two-stream network fusion for video action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1933–1941 (2016)
7.
Gaidon, A., Harchaoui, Z., Schmid, C.: Actom sequence models for efficient action detection. In: CVPR 2011, pp. 3201–3208. IEEE (2011)
8.
Jain, M., Jegou, H., Bouthemy, P.: Better exploiting motion for better action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2555–2562 (2013)
9.
Jhuang, H., Gall, J., Zuffi, S., Schmid, C., Black, M.J.: Towards understanding action recognition. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3192–3199 (2013)
10.
Lee, I., Kim, D., Kang, S., Lee, S.: Ensemble deep learning for skeleton-based action recognition using temporal sliding LSTM networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1012–1020 (2017)
11.
Li, C., Zhong, Q., Xie, D., Pu, S.: Skeleton-based action recognition with convolutional neural networks. In: 2017 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), pp. 597–600. IEEE (2017)
12.
Li, C., Cui, Z., Zheng, W., Xu, C., Yang, J.: Spatio-temporal graph convolution for skeleton based action recognition. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
13.
Li, W., Zhang, Z., Liu, Z.: Action recognition based on a bag of 3D points. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, pp. 9–14. IEEE (2010)
14.
Liu, J., Shahroudy, A., Xu, D., Kot, A.C., Wang, G.: Skeleton-based action recognition using spatio-temporal lstm network with trust gates. IEEE Trans. Pattern Anal. Mach. Intell. 40(12), 3007–3021 (2017)CrossRef
15.
Luvizon, D.C., Tabia, H., Picard, D.: Learning features combination for human action recognition from skeleton sequences. Pattern Recogn. Lett. 99, 13–20 (2017)CrossRef
16.
Pirsiavash, H., Ramanan, D.: Parsing videos of actions with segmental grammars. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 612–619 (2014)
17.
Rhif, M., Wannous, H., Farah, I.R.: Action recognition from 3D skeleton sequences using deep networks on lie group features. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 3427–3432. IEEE (2018)
18.
Sánchez, J., Perronnin, F., Mensink, T., Verbeek, J.: Image classification with the fisher vector: theory and practice. Int. J. Comput. Vis. 105(3), 222–245 (2013)MathSciNetCrossRef
19.
Si, C., Chen, W., Wang, W., Wang, L., Tan, T.: An attention enhanced graph convolutional lstm network for skeleton-based action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1227–1236 (2019)
20.
Si, C., Jing, Y., Wang, W., Wang, L., Tan, T.: Skeleton-based action recognition with spatial reasoning and temporal stack learning. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 103–118 (2018)CrossRef
21.
Slama, R., Wannous, H., Daoudi, M., Srivastava, A.: Accurate 3D action recognition using learning on the grassmann manifold. Pattern Recogn. 48(2), 556–567 (2015)CrossRef
22.
Sun, S., Kuang, Z., Sheng, L., Ouyang, W., Zhang, W.: Optical flow guided feature: a fast and robust motion representation for video action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1390–1399 (2018)
23.
Tran, A., Cheong, L.F.: Two-stream flow-guided convolutional attention networks for action recognition. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3110–3119 (2017)
24.
Vemulapalli, R., Arrate, F., Chellappa, R.: Human action recognition by representing 3D skeletons as points in a lie group. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 588–595 (2014)
25.
Wang, H., Kläser, A., Schmid, C., Liu, C.L.: Dense trajectories and motion boundary descriptors for action recognition. Int. J. Comput. Vis. 103(1), 60–79 (2013)MathSciNetCrossRef
26.
Wang, H., Schmid, C.: Action recognition with improved trajectories. In: Proceedings of the IEEE international Conference on Computer Vision, pp. 3551–3558 (2013)
27.
Wang, H., Wang, L.: Modeling temporal dynamics and spatial configurations of actions using two-stream recurrent neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 499–508 (2017)
28.
Wang, J., Liu, Z., Wu, Y., Yuan, J.: Mining actionlet ensemble for action recognition with depth cameras. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1290–1297. IEEE (2012)
29.
Wang, L., Xiong, Y., Wang, Z., Qiao, Y.: Towards good practices for very deep two-stream convnets. arXiv preprint arXiv:​1507.​02159 (2015)
30.
Xia, L., Chen, C.C., Aggarwal, J.K.: View invariant human action recognition using histograms of 3d joints. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 20–27. IEEE (2012)
31.
Zanfir, M., Leordeanu, M., Sminchisescu, C.: The moving pose: an efficient 3D kinematics descriptor for low-latency action recognition and detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2752–2759 (2013)
32.
Zhu, Y., Chen, W., Guo, G.: Fusing spatiotemporal features and joints for 3D action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 486–491 (2013)
Metadaten
Titel
Real-Time Recognition of Daily Actions Based on 3D Joint Movements and Fisher Encoding
verfasst von
Panagiotis Giannakeris
Georgios Meditskos
Konstantinos Avgerinakis
Stefanos Vrochidis
Ioannis Kompatsiaris
Copyright-Jahr
2020
Buch
MultiMedia Modeling

Print ISBN: 978-3-030-37733-5

Electronic ISBN: 978-3-030-37734-2

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-37734-2_49

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