The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Detecting activities of daily living in first-person camera views
Abstract
We present a novel dataset and novel algorithms for the problem of detecting activities of daily living (ADL) in firstperson camera views. We have collected a dataset of 1 million frames of dozens of people performing unscripted, everyday activities. The dataset is annotated with activities, object tracks, hand positions, and interaction events. ADLs differ from typical actions in that they can involve long-scale temporal structure (making tea can take a few minutes) and complex object interactions (a fridge looks different when its door is open). We develop novel representations including (1) temporal pyramids, which generalize the well-known spatial pyramid to approximate temporal correspondence when scoring a model and (2) composite object models that exploit the fact that objects look different when being interacted with. We perform an extensive empirical evaluation and demonstrate that our novel representations produce a two-fold improvement over traditional approaches. Our analysis suggests that real-world ADL recognition is “all about the objects,” and in particular, “all about the objects being interacted with.”
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Self-citationBeyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
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Learning to recognize objects in egocentric activities
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Ego4D: Around the World in 3,000 Hours of Egocentric Video
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Anticipating Human Activities Using Object Affordances for Reactive Robotic Response
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Recognizing Detailed Human Context in the Wild from Smartphones and Smartwatches
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Furnari, A., & Farinella, G. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00635 · Cited by 182
“Specifically, we build on previous ideas investigated in the context of action recognition such as the use of multiple modalities for video analysis [49], the use of Temporal Segment Networks [61] as a principled way to train CNNs for action recognition, as well as the explicit encoding of object-based features [11, 3…”
BackgroundMethodEgo-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
Grauman, K., Westbury, A., Torresani, L., Kitani, K., Malik, J., Afouras, T., Ashutosh, K., Baiyya, V., Bansal, S., Boote, B., et al. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01834 · Cited by 182
Grounded Human-Object Interaction Hotspots From Video
Nagarajan, T., Feichtenhofer, C., & Grauman, K. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00878 · Cited by 147
Modeling Actions through State Changes
Fathi, A., & Rehg, J. M. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.333 · Cited by 145
A-Wristocracy: Deep learning on wrist-worn sensing for recognition of user complex activities
Vepakomma, P., De, D., Das, S. K., & Bhansali, S. (2015) · 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) · doi:10.1109/bsn.2015.7299406 · Cited by 136
Mathur, A., Lane, N. D., Bhattacharya, S., Boran, A., Forlivesi, C., & Kawsar, F. (2017) · Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services · doi:10.1145/3081333.3081359 · Cited by 128
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Recognizing object affordances in terms of spatio-temporal object-object relationships
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Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
Grauman, K., Westbury, A., Torresani, L., Kitani, K., Malik, J., Afouras, T., Ashutosh, K., Baiyya, V., Bansal, S., Boote, B., et al. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02557-6 · Cited by 3
Metadata
- Authors
- Pirsiavash, H., & Ramanan, D.
- Venue
- 2012 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2012-06
- Pages
- 2847-2854
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2165605600
- Cited by
- 649
- References
- 5
- FWCI
- 35.10
- Type
- conference paper
- Language
- en
- Open access
- closed
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