Information theoretic focal length selection for real-time active 3D object tracking
Abstract
Active object tracking, for example, in surveillance tasks, becomes more and more important these days. Besides the tracking algorithms themselves methodologies have to be developed for reasonable active control of the degrees of freedom of all involved cameras. We present an information theoretic approach that allows the optimal selection of the focal lengths of two cameras during active 3D object tracking. The selection is based on the uncertainty in the 3D estimation. This allows us to resolve the trade-off between small and large focal length: in the former case, the chance is increased to keep the object in the field of view of the cameras. In the latter one, 3D estimation becomes more reliable. Also, more details are provided, for example for recognizing the objects. Beyond a rigorous mathematical framework we present real-time experiments demonstrating that we gain an improvement in 3D trajectory estimation by up to 42% in comparison with tracking using a fixed focal length.
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Active cameras can be used in AIoT systems to capture salient and high-resolution humans and vehicles by adjusting orientation and focal length according to target location.
“Moreover, to capture salient and high-resolution humans and vehicles, active cameras can be used in AIoT systems by adjusting the orientation and focal length according to target location [234], [235].”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [234]
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Zhang, J., & Tao, D. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3039359 · Cited by 774 · see citing works
Information-theoretic active scene exploration
Sommerlade, E., & Reid, I. (2008) · 2008 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2008.4587522 · Cited by 65 · see citing works
- Authors
- Denzler, Zobel, & Niemann
- Affiliations
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- Venue
- Proceedings Ninth IEEE International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2003
- Pages
- 400-407 vol.1
- OpenAlex
- W3139985820
- Cited by
- 53
- References
- 0
- Citations by year
- 2025: 1 · 2022: 3 · 2021: 1 · 2020: 2 · 2018: 1 · 2017: 3 · 2016: 2 · 2015: 4 · 2014: 4 · 2013: 3
- FWCI
- 1.72
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- information theory, camera control, multi-camera systems, surveillance, real-time tracking, 3D trajectory estimation, object recognition
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