Matching tracking sequences across widely separated cameras

In this paper, we present a new solution to the problem of matching tracking sequences across different cameras. Unlike snapshot-based appearance matching which matches objects by a single image, we focus on sequence matching to alleviate the uncertainties brought by segmentation errors and partial...

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Bibliographic Details
Published in2008 15th IEEE International Conference on Image Processing pp. 765 - 768
Main Authors Yinghao Cai, Kaiqi Huang, Tieniu Tan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2008
Subjects
Bayesian inference
Bayesian methods
Cameras
Dominant color representation
Face recognition
Hierarchical appearance matching
Humans
Image matching
Image segmentation
Lighting
Pattern matching
Robustness
Uncertainty
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Summary:In this paper, we present a new solution to the problem of matching tracking sequences across different cameras. Unlike snapshot-based appearance matching which matches objects by a single image, we focus on sequence matching to alleviate the uncertainties brought by segmentation errors and partial occlusions. By incorporating multiple snapshots of the same object, the influence of the variation is alleviated. At the training stage, given the sequence of a queried person under one camera, the appearance model is formulated by concatenating feature vectors with the majority of votes over the sequence. At the testing stage, Bayesian inference is incorporated into the identification framework to accumulate the temporal information in the sequence. Experimental results demonstrate the effectiveness of the proposed method.
ISBN:9781424417650
1424417651
ISSN:1522-4880
2381-8549
DOI:10.1109/ICIP.2008.4711867