Movement Human Actions Recognition Based on Machine Learning
Open accessAbstract
In this paper, the main technologies of foreground detection, feature description and extraction, movement behavior classification and recognition were introduced. Based on optical flow for movement objects detection, optical flow energy image was put forward for movement feature expression and region convolutional neural networks was adopt to choose features and decrease dimension. Then support vector machine classifier was trained and used to classify and recognize actions. After training and testing on public human actions database, the experiment result showed that the method could effectively distinguish human actions and significantly improved the recognition accuracy of human actions. And for the different situations of camera lens drawing near, pulling away or slight movement of camera, the solution had recognition effect as well. At last, this scheme was applied to intelligent video surveillance system, which was used to identify abnormal behavior and alarm. The abnormal behaviors of faint, smashing car, robbery and fighting were defined in the system. In running of the system, it obtained satisfactory recognition results.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
With the advancement of deep learning technology, manual feature extraction is no longer necessary, and performance improvement in complicated human activity identification is possible.
“With the advancement of DL technology, manual feature extraction is no longer necessary, and performance improvement in complicated human activity identification is possible [150].”
From Review of Wearable Devices and Data Collection Considerations for Connected Health (2021) · cites this paper as [150]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
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 · see citing works
What is a support vector machine?
Noble, W. S. (2006) · Nature Biotechnology · doi:10.1038/nbt1206-1565 · Cited by 4,307 · see citing works
Performance of optical flow techniques
Barron, J. L., Fleet, D. J., & Beauchemin, S. S. (1994) · International Journal of Computer Vision · doi:10.1007/bf01420984 · Cited by 4,305 · see citing works
The recognition of human movement using temporal templates
Bobick, A., & Davis, J. W. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.910878 · Cited by 2,830 · see citing works
A Review on Video-Based Human Activity Recognition
Ke, S.-R., Thuc, H. L. U., Lee, Y.-J., Hwang, J., Yoo, J., & Choi, K.-H. (2013) · Computers · doi:10.3390/computers2020088 · Cited by 436 · see citing works
Matching shape sequences in video with applications in human movement analysis
Veeraraghavan, A., Roy–Chowdhury, A. K., & Chellappa, R. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.246 · Cited by 338 · see citing works
Robust view transformation model for gait recognition
Zheng, S., Zhang, J., Huang, K., He, R., & Tan, T. (2011) · 2011 18th IEEE International Conference on Image Processing · doi:10.1109/icip.2011.6115889 · Cited by 301 · see citing works
Understanding Transit Scenes: A Survey on Human Behavior-Recognition Algorithms
Candamo, J., Shreve, M., Goldgof, D. B., Sapper, D., & Kasturi, R. (2009) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2009.2030963 · Cited by 300 · see citing works
Handbook of Pattern Recognition and Computer Vision
Chen, C. H. (2015) · WORLD SCIENTIFIC eBooks · doi:10.1142/9503 · Cited by 144 · see citing works
Training a Support Vector Machine in the Primal
Chapelle, O. (2007) · The MIT Press eBooks · doi:10.7551/mitpress/7496.003.0004 · Cited by 57 · see citing works
Intelligent Video Surveillance
Held, C., Krumm, J., Markel, P., & Schenke, R. P. (2012) · Computer · doi:10.1109/mc.2012.97 · Cited by 45 · see citing works
Moving target detection for sense and avoid using regional phase correlation
May, K., & Krouglicof, N. (2013) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2013.6631256 · Cited by 11 · see citing works
Nixon, M. S., & Aguado, A. S. (2002) · Elsevier eBooks · doi:10.1016/b978-0-08-050625-8.50011-0 · Cited by 8 · see citing works
Ji, E., Gu, G., Bai, L., et al. (2014) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2072146 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
- Ke S R, Thuc H L U, Lee Y J, et al. A Review on Video-Based Human Activity Recogni- tion[J]. Computers, 2013, 2(2):88-131. https://doi.org/10.3390/computers2020088 · DOI
- Dalal N, Triggs B, Schmid C. Human Detection Using Oriented Histograms of Flow and Appearance[M]// Computer Vision -ECCV 2006. Springer Berlin Heidelberg, 2006:428- 441.
- Karpathy A, Toderici G, Shetty S, et al. Large-Scale Video Classification with Convolu- tional Neural Networks[C]// Computer Vision and Pattern Recognition. IEEE, 2014:1725- 1732.
- Dalal N, Triggs B. Histograms of Oriented Gradients for Human Detection[C] Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on. IEEE, 2005:886-893
Cited by
Sorted by citations
Review of Wearable Devices and Data Collection Considerations for Connected Health
Vijayan, V., Connolly, J. P., Condell, J., McKelvey, N., & Gardiner, P. (2021) · Sensors · doi:10.3390/s21165589 · Cited by 435 · see citing works
- Authors
- Xu, H., Li, L., Fang, M., & Zhang, F.
- Affiliations
- Changchun University of Science and Technology (CN)
- Venue
- International Journal of Online and Biomedical Engineering (iJOE)
- Published
- 2018-04-26
- Volume
- 14
- Issue
- 04
- Pages
- 193-210
- ISSN
- 2626-8493 (ISSN-L)
- OpenAlex
- W2801958295
- Cited by
- 34
- References
- 16
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 4 · 2023: 7 · 2022: 5 · 2021: 5 · 2020: 9 · 2019: 1
- FWCI
- 1.88
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- International Journal of Online and Biomedical Engineering (iJOE) (publishedVersion, cc-by)
- Funders
- Changchun University of Science and Technology
- National Social Science Fund of China
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Keywords
- human action recognition, foreground detection, optical flow, feature extraction, support vector machine, intelligent video surveillance, abnormal behavior detection, camera motion robustness
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
