The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Spatio-temporal relationship match: Video structure comparison for recognition of complex human activities
Abstract
Human activity recognition is a challenging task, especially when its background is unknown or changing, and when scale or illumination differs in each video. Approaches utilizing spatio-temporal local features have proved that they are able to cope with such difficulties, but they mainly focused on classifying short videos of simple periodic actions. In this paper, we present a new activity recognition methodology that overcomes the limitations of the previous approaches using local features. We introduce a novel matching, spatio-temporal relationship match, which is designed to measure structural similarity between sets of features extracted from two videos. Our match hierarchically considers spatio-temporal relationships among feature points, thereby enabling detection and localization of complex non-periodic activities. In contrast to previous approaches to `classify' videos, our approach is designed to `detect and localize' all occurring activities from continuous videos where multiple actors and pedestrians are present. We implement and test our methodology on a newly-introduced dataset containing videos of multiple interacting persons and individual pedestrians. The results confirm that our system is able to recognize complex non-periodic activities (e.g. `push' and `hug') from sets of spatio-temporal features even when multiple activities are present in the scene.
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Citations
Cites
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“Capturing this structure may lead to better classification performance [10, 75, 76].”
BackgroundSensor-based and vision-based human activity recognition: A comprehensive survey
Minh Dang, L., Min, K., Wang, H., Jalil Piran, M., Hee Lee, C., & Moon, H. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107561 · Cited by 730
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A survey of video datasets for human action and activity recognition
Chaquet, J. M., Carmona, E. J., & Fernández-Caballero, A. (2013) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2013.01.013 · Cited by 455
Slow feature analysis for human action recognition
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Connectionist Temporal Modeling for Weakly Supervised Action Labeling
Huang, D.-A., Fei-Fei, L., & Niebles, J. C. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_9 · Cited by 226
Multi-user activity recognition: Challenges and opportunities
Li, Q., Gravina, R., Li, Y., Alsamhi, S. H., Sun, F., & Fortino, G. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.06.004 · Cited by 140
Context-Aware Activity Recognition and Anomaly Detection in Video
Zhu, Y., Nayak, N. M., & Roy-Chowdhury, A. K. (2013) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2012.2234722 · Cited by 121
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Temporal Sequence Modeling for Video Event Detection
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Progressively Parsing Interactional Objects for Fine Grained Action Detection
Ni, B., Yang, X., & Gao, S. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.116 · Cited by 69
Fine-grained activity classification in assembly based on multi-visual modalities
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Recognising complex activities with histograms of relative tracklets
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Metadata
- Authors
- Ryoo, M. S., & Aggarwal, J. K.
- Venue
- 2009 IEEE 12th International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2009-09
- Pages
- 1593-1600
- OpenAlex
- W2533503513
- Cited by
- 578
- References
- 1
- FWCI
- 26.99
- Type
- conference paper
- Language
- en
- Open access
- closed
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