  Pedestrian attribute recognition aims to assign multiple attributes to one
pedestrian image captured by a video surveillance camera. Although numerous
methods are proposed and make tremendous progress, we argue that it is time to
step back and analyze the status quo of the area. We review and rethink the
recent progress from three perspectives. First, given that there is no explicit
and complete definition of pedestrian attribute recognition, we formally define
and distinguish pedestrian attribute recognition from other similar tasks.
Second, based on the proposed definition, we expose the limitations of the
existing datasets, which violate the academic norm and are inconsistent with
the essential requirement of practical industry application. Thus, we propose
two datasets, PETA\textsubscript{$ZS$} and RAP\textsubscript{$ZS$}, constructed
following the zero-shot settings on pedestrian identity. In addition, we also
introduce several realistic criteria for future pedestrian attribute dataset
construction. Finally, we reimplement existing state-of-the-art methods and
introduce a strong baseline method to give reliable evaluations and fair
comparisons. Experiments are conducted on four existing datasets and two
proposed datasets to measure progress on pedestrian attribute recognition.
