An efficient pedestrian attribute recognition system under challenging conditions

In this work, an efficient pedestrian attribute recognition system is introduced. The system is based on a novel processing pipeline that combines the best-performing attribute extraction model with an efficient attribute filtering algorithm using keypoints of human pose. The attribute extraction models are developed based on several state-of-the-art deep networks via transfer learning techniques, including ResNet50, Swin-transformer, and ConvNeXt. Pre-trained models of these networks are fine-tuned using the Ensemble Pedestrian Attribute Recognition (EPAR) dataset. Several optimization techniques, including the advanced optimizer Adam with Decoupled Weight Decay Regularization (AdamW), Random Erasing (RE), and weighted loss functions, are adopted to solve issues of data unbalancing or challenging conditions like partial and occluded bodies. Experimental evaluations are performed via EPAR that contains 26 993 images of 1477 person IDs, most of which are in challenging conditions. The results show that the ConvNeXt-v2-B outperforms other networks; mean accuracy (mA) reaches 85.57%, and other indices are also the highest. The addition of AdamW or RE can improve accuracy by 1-2%. The use of new loss functions can solve the issue of data unbalancing, in which the accuracy of data-less attributes improves by a maximum of 14% in the best case. Significantly, when the attribute filtering algorithm is applied, the results are dramatically improved, and mA reaches an excellent value of 94.85%. Utilizing the state-of-the-art attribute extraction model with optimization techniques on the large-scale and diverse dataset and attribute filtering has shown a good approach and thus has a high potential for practical applications.