The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
A Scalable Approach to Activity Recognition based on Object Use
Abstract
We propose an approach to activity recognition based on detecting and analyzing the sequence of objects that are being manipulated by the user. In domains such as cooking, where many activities involve similar actions, object-use information can be a valuable cue. In order for this approach to scale to many activities and objects, however, it is necessary to minimize the amount of human-labeled data that is required for modeling. We describe a method for automatically acquiring object models from video without any explicit human supervision. Our approach leverages sparse and noisy readings from RFID tagged objects, along with common-sense knowledge about which objects are likely to be used during a given activity, to bootstrap the learning process. We present a dynamic Bayesian network model which combines RFID and video data to jointly infer the most likely activity and object labels. We demonstrate that our approach can achieve activity recognition rates of more than 80% on a real-world dataset consisting of 16 household activities involving 33 objects with significant background clutter. We show that the combination of visual object recognition with RFID data is significantly more effective than the RFID sensor alone. Our work demonstrates that it is possible to automatically learn object models from video of household activities and employ these models for activity recognition, without requiring any explicit human labeling.
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Citations
Cites
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
SURF: Speeded Up Robust Features
Bay, H., Tuytelaars, T., & Van Gool, L. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_32 · Cited by 14,955 · cited 1×
“SURF features [2]) could be used to replace SIFT features.”
MethodActivity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model
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“Temporal constraints on actions are addressed either through the use of probabilistic models such as HMM’s [4, 16] or SCFG’s [9], or through explicit temporal correlation methods [21, 26].”
Method
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Cited by
Sensor-Based Activity Recognition
Chen, L., Hoey, J., Nugent, C. D., Cook, D. J., & Yu, Z. (2012) · IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/tsmcc.2012.2198883 · Cited by 1,074
Detecting activities of daily living in first-person camera views
Pirsiavash, H., & Ramanan, D. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248010 · Cited by 649
Learning to recognize objects in egocentric activities
Fathi, A., Ren, X., & Rehg, J. M. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995444 · Cited by 557
Self-citationCombining embedded accelerometers with computer vision for recognizing food preparation activities
Stein, S., & McKenna, S. J. (2013) · Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing · doi:10.1145/2493432.2493482 · Cited by 448
Understanding egocentric activities
Fathi, A., Farhadi, A., & Rehg, J. M. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126269 · Cited by 384
Self-citation
Metadata
- Authors
- Wu, J., Osuntogun, A., Choudhury, T., Philipose, M., & Rehg, J. M.
- Venue
- 2007 IEEE 11th International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2007
- Pages
- 1-8
- OpenAlex
- W2155983176
- Cited by
- 324
- References
- 3
- FWCI
- 16.97
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Wu, J., Osuntogun, A., Choudhury, T., Philipose, M., & Rehg, J. M. (2007). A Scalable Approach to Activity Recognition based on Object Use. 2007 IEEE 11th International Conference on Computer Vision. https://doi.org/10.1109/iccv.2007.4408865
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