Improving Attribute-Based Person Retrieval by Using a Calibrated, Weighted, and Distribution-Based Distance Metric
Typically, person re-identification systems use so-called query images of a person to find occurrences of a person in surveillance footage. In real-world scenarios, however, often only witness descriptions and no images of a person-of-interest are available. In such cases, attribute-based retrieval can be performed based on pedestrian attribute recognition approaches. Current methods rely on calculating the Euclidean distance between query attributes and attribute predictions of gallery samples to compute ranking result lists. However, these methods do not consider the output distributions of the attribute classifier. We propose to do so by introducing a distance computation that remedies the effects of unbalanced distributions of attribute predictions. Moreover, we adapt a calibration technique to reduce the negative influences further. We also propose to weight the attributes during retrieval based on their prediction errors. In total, our approach surpasses the retrieval performance achieved by the Euclidean distance by a large margin on several datasets. In figures, we were able to increase the mAP on the RAP-2.0 and Market-1501 datasets by 4.2% and 2.4% points, respectively.