Research and Implementation of Pedestrian Attribute Recognition Algorithm Based on Deep Learning

In the realm of computer vision, recognizing pedestrian attributes is a crucial task, with the objective of deducing the identity characteristics of individuals on foot. This differs from conventional pedestrian detection methods in that it not only identifies the presence of pedestrians but also examines their attributes such as gender, age, and attire by scrutinizing their visual features. Not only can recognition identify the existence of pedestrians, but it can also delve into the analysis of pedestrian attributes. This paper proposes, explores, and implements a pedestrian attribute recognition algorithm grounded in deep convolutional neural networks. First, several datasets containing large-scale pedestrian images and their corresponding data are used for the recognition of pedestrian attributes. containing large-scale pedestrian images and their corresponding attribute labels are utilized. Through data pre-processing and enhancement techniques, the noise and inconsistency of pedestrian images are reduced. techniques, the noise and inconsistency of the data are.