Improving Pedestrian Attribute Recognition with Dense Feature Pyramid and Mixed Pooling

In the field of computer vision, pedestrian attribute recognition plays a crucial role in pedestrian detection and pedestrian re-identification. However, this task faces challenges such as blurry images, difficulty in recognizing fine-grained features, and overlooking relationships between pedestrian attributes. To address these challenges, we propose a novel method for pedestrian attribute recognition. Our method is based on convolutional neural networks and incorporates a feature pyramid structure that is specifically designed for the task of pedestrian attribute recognition (PAR). Additionally, we enhance feature information by employing multi-scale feature fusion. Furthermore, our proposed AIIM module facilitates interactions between different attributes by establishing both remote dependencies and short-range dependencies. Through comprehensive experimentation, we have validated the effectiveness of our method and achieved state-of-the-art results. Specifically, our method has achieved impressive average accuracies (mA) of 86.27%, 83.45%, and 81.56% on well-known datasets such as PETA, RAP, and PA100k, respectively.