Abstract
Human activity recognition refers to predict what a person is doing from series of the observation of person’s action and surrounding conditions using different techniques. It is an active research area providing personalized support for various applications and its association with a wide range of fields of study like medicinal services, dependable automation developing, and smart surveillance system. This paper provides an overview which gives idea about some existing research methods on human activity recognition. It describes a general view on the state of the art for human activity recognition and shows comparative studies between existing research which consist of various methods, evaluation criteria, and features. It also comprises benefits and limitations of various methods to provide researchers to propose new approaches.
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References
Zhang HB, Zhang YX, Zhong B, Lei Q, Yang L, Du JX, Chen DS (2019) A comprehensive survey of vision-based human action recognition methods. Sensors 19(5):1005
Ji S, Xu W, Yang M, Yu K (2013) 3D convolutional neural networks for human action recognition. IEEE Trans Pattern Anal Mach Intell 35(1):221–231
Liu Z, Zhang C, Tian Y (2016) 3D-based deep convolutional neural network for action recognition with depth sequences. In: Image and vision computing
Zhang P, Lan C, Xing J, Zeng W, Xue J, Zheng N (2019) View adaptive neural networks for high performance skeleton-based human action recognition. IEEE Trans Pattern Anal Mach Intell 41(8):1963–1978
Mehr HD, Polat H (2019) Human activity recognition in smart home with deep learning approach. In: 7th International Istanbul smart grids and cities congress and fair (ICSG), Istanbul, Turkey, pp 149–153
Kim TS, Reiter A (2017) Interpretable 3D human action analysis with temporal convolutional networks. In: IEEE conference on computer vision and pattern recognition workshops (CVPRW), Honolulu, HI, pp 1623–1631
An F (2018) Human action recognition algorithm based on adaptive initialization of deep learning model parameters and support vector machine. IEEE Access 6:59405–59421
Tomas A, Biswas KK (2017) Human activity recognition using combined deep architectures. In: IEEE 2nd international conference on signal and image processing (ICSIP), Singapore, pp 41–45
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Suthar, B., Gadhia, B. (2021). Human Activity Recognition Using Deep Learning: A Survey. In: Kotecha, K., Piuri, V., Shah, H., Patel, R. (eds) Data Science and Intelligent Applications. Lecture Notes on Data Engineering and Communications Technologies, vol 52. Springer, Singapore. https://doi.org/10.1007/978-981-15-4474-3_25
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DOI: https://doi.org/10.1007/978-981-15-4474-3_25
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