  Pedestrian Attribute Recognition (PAR) has aroused extensive attention due to
its important role in video surveillance scenarios. In most cases, the
existence of a particular attribute is strongly related to a partial region.
Recent works design complicated modules, e.g., attention mechanism and proposal
of body parts to localize the attribute corresponding region. These works
further prove that localization of attribute specific regions precisely will
help in improving performance. However, these part-information-based methods
are still not accurate as well as increasing model complexity which makes it
hard to deploy on realistic applications. In this paper, we propose a Deep
Template Matching based method to capture body parts features with less
computation. Further, we also proposed an auxiliary supervision method that use
human pose keypoints to guide the learning toward discriminative local cues.
Extensive experiments show that the proposed method outperforms and has lower
computational complexity, compared with the state-of-the-art approaches on
large-scale pedestrian attribute datasets, including PETA, PA-100K, RAP, and
RAPv2 zs.
