Pedestrian attribute classification in surveillance: Database and evaluation

Attributes are helpful to infer high-level semantic knowledge of pedestrians, thus improving the performance of pedestrian tracking, retrieval, re-identification, etc. However, current pedestrian databases are mainly for the pedestrian detection or tracking application, and semantic attribute annotations related to pedestrians are rarely provided. In this paper, we construct an Attributed Pedestrians in Surveillance (APiS) database with various scenes. The APiS 1.0 database includes 3661 images with 11 binary and 2 multi-class attribute annotations. Moreover, we develop an evaluation protocol for researchers to evaluate pedestrian attribute classification algorithms. With the APiS 1.0 database, we present two baseline methods, one for binary attribute classification and the other for multi-class attribute classification. For binary attribute classification, we train AdaBoost classifiers with color and texture features, while for multi-class attribute classification, we adopt a weighted K Nearest Neighbors (KNN) classifier with color features. Finally, we report and discuss the baseline performance on the APiS 1.0 database following the proposed evaluation protocol.