Improved Adaptive Gaussian Mixture Model for Background Subtraction
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Appearance Modeling for Tracking in Multiple Non-Overlapping Cameras
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Tracking people across disjoint camera views by an illumination-tolerant appearance representation
Machine Vision and Applications
Computer Vision and Image Understanding
Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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Cross-camera matching is often done using color features, which can compensate poses and viewpoint changes fairly well. On the downside, color features are very sensitive to illumination changes. In multi-camera systems, cameras are often installed at different physical sites and therefore illumination conditions are likely to differ, which highly influences the accuracy of the matching process. To overcome this mismatch, a Brightness Transfer Function can be trained to establish a mapping of brightness values between the different views. However, as soon as the lighting conditions do not match the training condition any longer, mapping accuracy tends to decrease again. In this work, an unsupervised approach for mitigating the effects of intra-camera illumination change is proposed. After applying such a procedure, the changing illumination over time has less impact on the fixed training stage and therefore inter-camera recognition performance improves.