Abstract
Having a reliable tool to predict the actions performed in a video can be very useful for intelligent security systems, for many applications related to robotics and for limiting human interactions with the system. In this work we present an architecture trained to predict the action present in digital video sequences. The proposed architecture consists of two main blocks: (i) a 3D backbone that extracts features from each frame of video sequence and (ii) a temporal pooling. In this case, we use Bidirectional Encoder Representations from Transformers (BERT) as technology for temporal pooling instead of a Temporal Global Average Pooling (TGAP). The output of the proposed architecture is the prediction of the action taking place in the video sequence. We use two different backbones, ip-CSN and ir-CSN, in order to evaluate the performance of the entire architecture on two publicly available datasets: HMDB-51, UCF-101. A comparison has been made with the most important architectures that constitute the state of the art for this task. We have obtained results that outperform the state of the art in terms of Top-1 and Top-3 accuracy.
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References
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)
Crasto, N., Weinzaepfel, P., Alahari, K., Schmid, C.: Mars: motion-augmented RGB stream for action recognition. In: Proceedings of the IEEE Conference On Computer Vision Aand Pattern Recognition, pp. 7882–7891 (2019)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Diba, A., et al.: Temporal 3D convnets: new architecture and transfer learning for video classification. arXiv preprint arXiv:1711.08200 (2017)
Du, W., Wang, Y., Qiao, Y.: Recurrent spatial-temporal attention network for action recognition in videos. IEEE Trans. Image Process. 27(3), 1347–1360 (2017)
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)
Ghadiyaram, D., Tran, D., Mahajan, D.: Large-scale weakly-supervised pre-training for video action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 12046–12055 (2019)
Girdhar, R., Carreira, J., Doersch, C., Zisserman, A.: Video action transformer network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 244–253 (2019)
Girdhar, R., Ramanan, D.: Attentional pooling for action recognition. In: Advances in Neural Information Processing Systems, pp. 34–45 (2017)
Girdhar, R., Ramanan, D., Gupta, A., Sivic, J., Russell, B.: Actionvlad: learning spatio-temporal aggregation for action classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 971–980 (2017)
Hendrycks, D., Gimpel, K.: Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08415 (2016)
Hira, S., Das, R., Modi, A., Pakhomov, D.: Delta sampling R-bert for limited data and low-light action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 853–862 (2021)
Kalfaoglu, M., Kalkan, S., Alatan, A.A.: Late temporal modeling in 3D CNN architectures with bert for action recognition. arXiv preprint arXiv:2008.01232 (2020)
Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., Serre, T.: HMDB: a large video database for human motion recognition. In: Proceedings of the International Conference on Computer Vision (ICCV) (2011)
Lin, J., Gan, C., Han, S.: TSM: temporal shift module for efficient video understanding. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 7083–7093 (2019)
Long, X., Gan, C., De Melo, G., Wu, J., Liu, X., Wen, S.: Attention clusters: purely attention based local feature integration for video classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7834–7843 (2018)
Loshchilov, I., Hutter, F.: Fixing weight decay regularization in Adam (2018)
Nagrani, A., Sun, C., Ross, D., Sukthankar, R., Schmid, C., Zisserman, A.: Speech2action: cross-modal supervision for action recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10317–10326 (2020)
Piergiovanni, A., Angelova, A., Toshev, A., Ryoo, M.S.: Evolving space-time neural architectures for videos. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1793–1802 (2019)
Piergiovanni, A., Ryoo, M.S.: Representation flow for action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9945–9953 (2019)
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., Ray, J., Shou, Z., Chang, S.F., Paluri, M.: Convnet architecture search for spatiotemporal feature learning. arXiv preprint arXiv:1708.05038 (2017)
Tran, D., Wang, H., Torresani, L., Feiszli, M.: Video classification with channel-separated convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5552–5561 (2019)
Tran, D., Wang, H., Torresani, L., Ray, J., LeCun, Y., Paluri, M.: A closer look at spatiotemporal convolutions for action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6450–6459 (2018)
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., Koniusz, P., Huynh, D.Q.: Hallucinating IDT descriptors and I3D optical flow features for action recognition with CNNS. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 8698–8708 (2019)
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
Wang, L., et al.: Temporal segment networks for action recognition in videos. IEEE Trans. Pattern Anal. Mach. Intell. 41(11), 2740–2755 (2018)
Yue-Hei Ng, J., Hausknecht, M., Vijayanarasimhan, S., Vinyals, O., Monga, R., Toderici, G.: Beyond short snippets: deep networks for video classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4694–4702 (2015)
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Mazzeo, P.L., Spagnolo, P., Fasano, M., Distante, C. (2022). Human Action Recognition with Transformers. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds) Image Analysis and Processing – ICIAP 2022. ICIAP 2022. Lecture Notes in Computer Science, vol 13233. Springer, Cham. https://doi.org/10.1007/978-3-031-06433-3_20
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