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Cited by 504

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Using adaptive tracking to classify and monitor activities in a site

Grimson, W.E.L., Stauffer, C., Romano, R., Lee, L.·2002·Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231)
DOI 10.1109/cvpr.1998.698583
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Abstract

We describe a vision system that monitors activity in a site over extended periods of time. The system uses a distributed set of sensors to cover the site, and an adaptive tracker detects multiple moving objects in the sensors. Our hypothesis is that motion tracking is sufficient to support a range of computations about site activities. We demonstrate using the tracked motion data to calibrate the distributed sensors, to construct rough site models, to classify detected objects, to learn common patterns of activity for different object classes, and to detect unusual activities.

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Metadata

Authors
Grimson, W. E. L., Stauffer, C., Romano, R., & Lee, L.
Venue
Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231)
Publisher
IEEE Comput. Soc
Published
2002-11-27
Pages
22-29
ISSN
1063-6919 (ISSN-L)
DOI
10.1109/cvpr.1998.698583
OpenAlex
W2097350683
Cited by
504
References
0
FWCI
28.15
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Using adaptive tracking to classify and monitor activities in a site. https://app.cochat.ai/papers/10.1109/cvpr.1998.698583
The paper
Grimson, W. E. L., Stauffer, C., Romano, R., & Lee, L. (2002). Using adaptive tracking to classify and monitor activities in a site. Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231). https://doi.org/10.1109/cvpr.1998.698583

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.