A Multi-Granularity Feature Fusion Model for Pedestrian Attribute Recognition

Pedestrian attributes are defined as pedestrian appearance features which can be observed directly, usually including gender, age, clothing, etc. The purpose of pedestrian attribute recognition (PAR) is to perform semantic analysis on a given pedestrian image, which is widely used in person reidentification [1] and human detection [2]. Owing to the influence of factors such as changeable postures, occlusion, uneven lighting and different perspectives, some features with poor semantics in pedestrian images are too weak to learn, and thus the classification becomes more difficult.