The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Tracking multiple humans in complex situations
Abstract
Tracking multiple humans in complex situations is challenging. The difficulties are tackled with appropriate knowledge in the form of various models in our approach. Human motion is decomposed into its global motion and limb motion. In the first part, we show how multiple human objects are segmented and their global motions are tracked in 3D using ellipsoid human shape models. Experiments show that it successfully applies to the cases where a small number of people move together, have occlusion, and cast shadow or reflection. In the second part, we estimate the modes (e.g., walking, running, standing) of the locomotion and 3D body postures by making inference in a prior locomotion model. Camera model and ground plane assumptions provide geometric constraints in both parts. Robust results are shown on some difficult sequences.
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Cites
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CONDENSATION—Conditional Density Propagation for Visual Tracking
Isard, M., & Blake, A. (1998) · International Journal of Computer Vision · doi:10.1023/a:1008078328650 · Cited by 4,886
Pfinder: real-time tracking of the human body
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The recognition of human movement using temporal templates
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Cited by
Monocular Pedestrian Detection: Survey and Experiments
Enzweiler, M., & Gavrila, D. M. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.260 · Cited by 1,213
Video object tracking using adaptive Kalman filter
Weng, S.-K., Kuo, C.-M., & Tu, S.-K. (2006) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2006.03.004 · Cited by 323
A daily behavior enabled hidden Markov model for human behavior understanding
Chung, P.-C., & Liu, C.-D. (2008) · Pattern Recognition · doi:10.1016/j.patcog.2007.10.022 · Cited by 130
The Behavioral Space of Zebrafish Locomotion and Its Neural Network Analog
Girdhar, K., Gruebele, M., & Chemla, Y. R. (2015) · PLoS ONE · doi:10.1371/journal.pone.0128668 · Cited by 60
“With the advent of faster speed in computers, we can now automatically track animals or humans and measure the behavioral phenotypes of flies [2], worms [10], ants [11], humans [12] etc.”
Background
Metadata
- Authors
- Zhao, T., & Nevatia, R.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2004-09
- Volume
- 26
- Issue
- 9
- Pages
- 1208-1221
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2122190623
- Cited by
- 490
- References
- 33
- FWCI
- 34.64
- Type
- journal article
- Language
- en
- Open access
- closed
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Data sources
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).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
