A Review of Deep Learning Techniques for Human Activity Recognition
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-27409-1_28
Reference25 articles.
1. Noori, F.M., Wallace, B., Uddin, Md.Z., Torresen, J.: A robust human activity recognition approach using OpenPose, motion features, and deep recurrent neural network. In: Felsberg, M., Forssén, P.-E., Sintorn, I.-M., Unger, J. (eds.) Image Analysis, pp. 299–310. Springer International Publishing, Cham (2019)
2. Wan, S., Qi, L., Xu, X., Tong, C., Gu, Z.: Deep learning models for real-time human activity recognition with smartphones. Mob. Netw. Appl. 25(2), 743–755 (2019). https://doi.org/10.1007/s11036-019-01445-x
3. Gnouma, M., Ladjailia, A., Ejbali, R., Zaied, M.: Stacked sparse autoencoder and history of binary motion image for human activity recognition. Multim. Tools Appl. 78(2), 2157–2179 (2018). https://doi.org/10.1007/s11042-018-6273-1
4. Vishwakarma, D.K., Dhiman, C.: A unified model for human activity recognition using spatial distribution of gradients and difference of Gaussian kernel. Vis. Comput. 35(11), 1595–1613 (2018). https://doi.org/10.1007/s00371-018-1560-4
5. Chaudhary, S., Dudhane, A., Patil, P., Murala, S.: Pose guided dynamic image network for human action recognition in Person centric videos. In: 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–8 (2019)
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