An Adaptive Learning Method for Target Tracking across Multiple Cameras
Resource
Proc. IEEE Computer Society International Conference on Computer Vision and Pattern Recognition
Journal
26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR
Pages
1
Date Issued
2008
Date
2008
Author(s)
Abstract
This paper proposes an adaptive learning method for tracking targets across multiple cameras with disjoint views. Two visual cues are usually employed for tracking targets across cameras: spatio-temporal cue and appearance cue. To learn the relationships among cameras, traditional methods used batch-learning procedures or hand-labeled correspondence, which can work well only within a short period of time. In this paper, we propose an unsupervised method which learns both spatio-temporal relationships and appearance relationships adaptively and can be applied to long-term monitoring. Our method performs target tracking across multiple cameras while also considering the environment changes, such as sudden lighting changes. Also, we improve the estimation of spatio-temporal relationships by using the prior knowledge of camera network topology. ©2008 IEEE.
Other Subjects
Artificial intelligence; Cameras; Computer vision; Education; Electric network topology; Feature extraction; Image processing; Pattern recognition; Targets; Video cameras; Adaptive learning method; Camera networks; Learning procedures; Lighting changes; Long-term monitoring; Multiple cameras; Prior knowledge; Spatio-Temporal; Spatio-temporal relationships; Tracking targets; Unsupervised method; Visual cues; Target tracking
Type
conference paper
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