Dense appearance modeling and efficient learning of camera transitions for person re-identification
One central task in many visual surveillance scenarios is person re-identification, i.e., recognizing an individual person across a network of spatially disjoint cameras. Most successful recognition approaches are either based on direct modeling of the human appearance or on machine learning. In this work, we aim at taking advantage of both directions of research. On the one hand side, we compute a descriptive appearance representation encoding the vertical color structure of pedestrians. To improve the classification results, we additionally estimate the transition between two cameras using a pair-wisely estimated metric. In particular, we introduce 4D spatial color histograms and adopt Large Margin Nearest Neighbor (LMNN) metric learning. The approach is demonstrated for two publicly available datasets, showing competitive results, however, on lower computational costs.