Abstract
Deep Convolutional Neural Networks (CNNs) have achieved great success in object recognition. However, they are difficult to capture the long-range temporal information, which plays an important role for action recognition in videos. To overcome this issue, a two-stream architecture including spatial and temporal segments based CNNs is widely used recently. However, the relationship among the segments is not sufficiently investigated. In this paper, we proposed to combine multiple segments by a fully connected layer in a deep CNN model for the whole action video. Moreover, the four streams (i.e., RGB, RGB differences, optical flow, and warped optical flow) are carefully integrated with a linear combination, and the weights are optimized on the validation datasets. We evaluate the recognition accuracy of the proposed method on two benchmark datasets of UCF101 and HMDB51. The extensive experimental results demonstrate encouraging results of our proposed method. Specifically, the proposed method improves the accuracy of action recognition in videos obviously (e.g., compared with the baseline, the accuracy is improved from 94.20% to 97.30% and from 69.40% to 77.99% on the dataset UCF101 and HMDB51, respectively). Furthermore, the proposed method can obtain the competitive accuracy to the state-of-the-art method of the 3D convolutional operation, but with much fewer parameters.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bilen, H., Fernando, B., Gavves, E., Vedaldi, A., Gould, S.: Dynamic image networks for action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3034–3042 (2016)
Carreira, J., Zisserman, A.: Quo vadis, action recognition? A new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017)
Donahue, J., et al.: Long-term recurrent convolutional networks for visual recognition and description. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2625–2634 (2015)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, Hoboken (2001)
Feichtenhofer, C., Pinz, A., Wildes, R.: Spatiotemporal residual networks for video action recognition. In: Advances in Neural Information Processing Systems, pp. 3468–3476 (2016)
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)
Huang, K., Hussain, A., Wang, Q., Zhang, R.: Deep Learning: Fundamentals, Theory and Applications. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-06073-2. ISBN 978-3-030-06072-5
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L.: Large-scale video classification with convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1725–1732 (2014)
Kay, W., et al.: The kinetics human action video dataset. arXiv preprint. arXiv:1705.06950 (2017)
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)
Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., Serre, T.: HMDB: a large video database for human motion recognition. In: 2011 International Conference on Computer Vision, pp. 2556–2563. IEEE (2011)
Santoro, A., et al.: A simple neural network module for relational reasoning. In: Advances in Neural Information Processing Systems, pp. 4967–4976 (2017)
Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: Advances in Neural Information Processing Systems, pp. 568–576 (2014)
Soomro, K., Zamir, A.R., Shah, M.: UCF101: a dataset of 101 human actions classes from videos in the wild. arXiv preprint. arXiv:1212.0402 (2012)
Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3D convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4489–4497 (2015)
Wang, H., Schmid, C.: Action recognition with improved trajectories. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3551–3558 (2013)
Wang, L., Qiao, Y., Tang, X.: Action recognition with trajectory-pooled deep-convolutional descriptors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4305–4314 (2015)
Wang, L., et al.: Temporal segment networks: towards good practices for deep action recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 20–36. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_2
Xiong, Y.: TSN Pre-trained Models on Kinetics Dataset (2017). http://yjxiong.me/others/kinetics_action/
Acknowledgement
The work was partially supported by the following: National Natural Science Foundation of China under no. 61876155, and 61876154; The Natural Science Foundation of the Jiangsu Higher Education Institutions of China under no. 17KJD520010; Suzhou Science and Technology Program under no. SYG201712, SZS201613; Natural Science Foundation of Jiangsu Province BK20181189 and BK20181190; Key Program Special Fund in XJTLU under no. KSF-A-01, KSF-P-02, KSF-E-26, and KSF-A-10; XJTLU Research Development Fund RDF-16-02-49.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Fang, Y., Zhang, R., Wang, QF., Huang, K. (2020). Action Recognition in Videos with Temporal Segments Fusions. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2019. Lecture Notes in Computer Science(), vol 11691. Springer, Cham. https://doi.org/10.1007/978-3-030-39431-8_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-39431-8_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-39430-1
Online ISBN: 978-3-030-39431-8
eBook Packages: Computer ScienceComputer Science (R0)