The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
A Review on Human Activity Recognition Using Vision-Based Method
Open accessAbstract
Human activity recognition (HAR) aims to recognize activities from a series of observations on the actions of subjects and the environmental conditions. The vision-based HAR research is the basis of many applications including video surveillance, health care, and human-computer interaction (HCI). This review highlights the advances of state-of-the-art activity recognition approaches, especially for the activity representation and classification methods. For the representation methods, we sort out a chronological research trajectory from global representations to local representations, and recent depth-based representations. For the classification methods, we conform to the categorization of template-based methods, discriminative models, and generative models and review several prevalent methods. Next, representative and available datasets are introduced. Aiming to provide an overview of those methods and a convenient way of comparing them, we classify existing literatures with a detailed taxonomy including representation and classification methods, as well as the datasets they used. Finally, we investigate the directions for future research.
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Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Fischler, M. A., & Bolles, R. C. (1981) · Communications of the ACM · doi:10.1145/358669.358692 · Cited by 25,893
Speeded-Up Robust Features (SURF)
Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2007.09.014 · Cited by 13,557 · cited 1×
“The speeded-up robust features (SURF) [47] approach is a scale and rotation invariant detector and descriptor.”
MethodMean shift: a robust approach toward feature space analysis
Comaniciu, D., & Meer, P. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.1000236 · Cited by 11,305
Shi, J., & Tomasi (1994) · Proceedings of IEEE Conference on Computer Vision and Pattern Recognition CVPR-94 · doi:10.1109/cvpr.1994.323794 · Cited by 7,724 · cited 1×
“Lucas-Kanade-Tomasi (LKT) feature tracker [22, 23] can be used to obtain the optical fl ow.”
Method3D Convolutional Neural Networks for Human Action Recognition
Ji, S., Xu, W., Yang, M., & Yu, K. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.59 · Cited by 6,235
An introduction to hidden Markov models
Rabiner, L., & Juang, B. (1986) · IEEE ASSP Magazine · doi:10.1109/massp.1986.1165342 · Cited by 4,775
Comaniciu, D., Ramesh, V., & Meer, P. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2003.1195991 · Cited by 4,560
Pfinder: real-time tracking of the human body
Wren, C. R., Azarbayejani, A., Darrell, T., & Pentland, A. P. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.598236 · Cited by 4,169
Cited by
A survey on wearable sensor modality centred human activity recognition in health care
Wang, Y., Cang, S., & Yu, H. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.04.057 · Cited by 409
Arikumar, K., Prathiba, S., Alazab, M., Gadekallu, T., Pandya, S., Khan, J., & Moorthy, R. (2022) · Sensors · doi:10.3390/s22041377 · Cited by 168
“The wearable sensor collects all of the physical activities of an individual, which can be used to understand his/her behavior, and is helpful in activity recognition and classification problems [5,6].”
BackgroundRetractedIslam, N., Faheem, Y., Din, I. U., Talha, M., Guizani, M., & Khalil, M. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.05.059 · Cited by 154
Human Activity Recognition Based on Residual Network and BiLSTM
Li, Y., & Wang, L. (2022) · Sensors · doi:10.3390/s22020635 · Cited by 156
Remote Health Monitoring Systems for Elderly People: A Survey
Ahmed, S., Irfan, S., Kiran, N., Masood, N., Anjum, N., & Ramzan, N. (2023) · Sensors · doi:10.3390/s23167095 · Cited by 52
Metadata
- Authors
- Zhang, S., Wei, Z., Nie, J., Huang, L., Wang, S., & Li, Z.
- Venue
- Journal of Healthcare Engineering
- Publisher
- Wiley
- Published
- 2017
- Volume
- 2017
- Pages
- 1-31
- ISSN
- 2040-2295 (ISSN-L)
- 2040-2309
- OpenAlex
- W2736707111
- Cited by
- 328
- References
- 48
- FWCI
- 11.91
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Zhang, S., Wei, Z., Nie, J., Huang, L., Wang, S., & Li, Z. (2017). A Review on Human Activity Recognition Using Vision-Based Method. Journal of Healthcare Engineering. https://doi.org/10.1155/2017/3090343
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