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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The Behavioral Space of Zebrafish Locomotion and Its Neural Network Analog
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“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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