The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Hierarchical motion history images for recognizing human motion
Abstract
There has been increasing interest in computer analysis and recognition of human motion. Previously we presented an efficient real-time approach for representing human motion using a compact "motion history image" (MHI). Recognition was achieved by statistically matching moment-based features. To address previous problems related to global analysis and limited recognition, we present a hierarchical extension to the original MHI framework to compute dense (local) motion flow directly from the MHI. A hierarchical partitioning of motions by speed in an MHI pyramid enables efficient calculation of image motions using fixed-size gradient operators. To characterize the resulting motion field, a polar histogram of motion orientations is described. The hierarchical MHI approach remains a computationally inexpensive method for analysis of human motions.
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Cites
The Laplacian pyramid as a compact image code
Burt, P., & Adelson, E. (1983) · IEEE Transactions on Communications · doi:10.1109/tcom.1983.1095851 · Cited by 6,077
Cited by
Human motion tracking for rehabilitation-A survey
Zhou, H., & Hu, H. (2008) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2007.09.001 · Cited by 813
Metadata
- Authors
- Davis, J. W.
- Venue
- Proceedings IEEE Workshop on Detection and Recognition of Events in Video
- Publisher
- IEEE Comput. Soc
- Published
- 2002-11-13
- Pages
- 39-46
- OpenAlex
- W2138256834
- Cited by
- 172
- References
- 1
- FWCI
- 1.41
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Davis, J. W. (2002). Hierarchical motion history images for recognizing human motion. Proceedings IEEE Workshop on Detection and Recognition of Events in Video. https://doi.org/10.1109/event.2001.938864
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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