The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Unstructured human activity detection from RGBD images
Open accessAbstract
Being able to detect and recognize human activities is essential for several applications, including personal assistive robotics. In this paper, we perform detection and recognition of unstructured human activity in unstructured environments. We use a RGBD sensor (Microsoft Kinect) as the input sensor, and compute a set of features based on human pose and motion, as well as based on image and point-cloud information. Our algorithm is based on a hierarchical maximum entropy Markov model (MEMM), which considers a person's activity as composed of a set of sub-activities. We infer the two-layered graph structure using a dynamic programming approach. We test our algorithm on detecting and recognizing twelve different activities performed by four people in different environments, such as a kitchen, a living room, an office, etc., and achieve good performance even when the person was not seen before in the training set.1
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Histograms of Oriented Gradients for Human Detection
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McCallum, A., Freitag, D., & Pereira, F. C. N. (2000) · Cited by 1,315
Action MACH a spatio-temporal Maximum Average Correlation Height filter for action recognition
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Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition
Ranzato, M., Huang, F. J., Boureau, Y.-L., & LeCun, Y. (2007) · 2007 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2007.383157 · Cited by 1,086
Coupled hidden Markov models for complex action recognition
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A review of smart homes—Present state and future challenges
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Self-citation
Cited by
Enhanced Computer Vision With Microsoft Kinect Sensor: A Review
Han, J., Shao, L., Xu, D., & Shotton, J. (2013) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2013.2265378 · Cited by 1,367
Zhu, W., Lan, C., Xing, J., Zeng, W., Li, Y., Shen, L., & Xie, X. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10451 · Cited by 915
Anticipating Human Activities Using Object Affordances for Reactive Robotic Response
Koppula, H. S., & Saxena, A. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2430335 · Cited by 596
Self-citationLearning Actionlet Ensemble for 3D Human Action Recognition
Wang, J., Liu, Z., Wu, Y., & Yuan, J. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.198 · Cited by 502
ModDrop: Adaptive Multi-Modal Gesture Recognition
Neverova, N., Wolf, C., Taylor, G., & Nebout, F. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2461544 · Cited by 420
Metadata
- Authors
- Sung, J., Ponce, C., Selman, B., & Saxena, A.
- Venue
- 2012 IEEE International Conference on Robotics and Automation
- Publisher
- IEEE
- Published
- 2012-05
- Pages
- 842-849
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2017695267
- Cited by
- 511
- References
- 43
- FWCI
- 38.97
- Type
- conference paper
- Language
- en
- Open access
- green
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- Sung, J., Ponce, C., Selman, B., & Saxena, A. (2012). Unstructured human activity detection from RGBD images. 2012 IEEE International Conference on Robotics and Automation. https://doi.org/10.1109/icra.2012.6224591
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