adaptive color transformation for person re-identification in camera networks
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.
adaptive color transformation person re-identification brightness transfer function multi-camera matching