  Recognizing pedestrian attributes is an important task in the computer vision
community due to it plays an important role in video surveillance. Many
algorithms have been proposed to handle this task. The goal of this paper is to
review existing works using traditional methods or based on deep learning
networks. Firstly, we introduce the background of pedestrian attribute
recognition (PAR, for short), including the fundamental concepts of pedestrian
attributes and corresponding challenges. Secondly, we introduce existing
benchmarks, including popular datasets and evaluation criteria. Thirdly, we
analyze the concept of multi-task learning and multi-label learning and also
explain the relations between these two learning algorithms and pedestrian
attribute recognition. We also review some popular network architectures which
have been widely applied in the deep learning community. Fourthly, we analyze
popular solutions for this task, such as attributes group, part-based, etc.
Fifthly, we show some applications that take pedestrian attributes into
consideration and achieve better performance. Finally, we summarize this paper
and give several possible research directions for pedestrian attribute
recognition. We continuously update the following GitHub to keep tracking the
most cutting-edge related works on pedestrian attribute
recognition~\url{https://github.com/wangxiao5791509/Pedestrian-Attribute-Recognition-Paper-List}
