Pedestrian Attributes Recognition for UAV-Human

Pedestrian Attributes Recognition (PAR) aims to predict a set of attributes from a predefined list to describe the characteristics of a pedestrian. This paper addresses the challenge of attribute recognition in the UAV-Human dataset. In this dataset, there are two key factors constraining attribute recognition performance. Firstly, there is class imbalance in each attribute, and the distribution of classes differs significantly between the testing and training sets. Secondly, some attributes cannot be observed from the current perspective of the images. For example, we cannot observe the attribute of the backpack from a frontal image. To tackle these issues, this paper proposes Class Imbalance Correction Strategy (CIC) and ID-based Attribute Correction Strategy (IAC) to enhance attribute recognition performance. CIC balances training classes using undersampling and oversampling to match the class distribution in the testing set. IAC corrects misidentified attributes by leveraging person re-identification (ReID) techniques to obtain images of the same pedestrian from different viewpoints. Our approach significantly improves the accuracy of attribute recognition over the baseline method. The code is publicly available at https://github.com/laiping-lp/MMVRAC-Attribute-Recognition.