  In video surveillance, pedestrian retrieval (also called person
re-identification) is a critical task. This task aims to retrieve the
pedestrian of interest from non-overlapping cameras. Recently,
transformer-based models have achieved significant progress for this task.
However, these models still suffer from ignoring fine-grained, part-informed
information. This paper proposes a multi-direction and multi-scale Pyramid in
Transformer (PiT) to solve this problem. In transformer-based architecture,
each pedestrian image is split into many patches. Then, these patches are fed
to transformer layers to obtain the feature representation of this image. To
explore the fine-grained information, this paper proposes to apply vertical
division and horizontal division on these patches to generate
different-direction human parts. These parts provide more fine-grained
information. To fuse multi-scale feature representation, this paper presents a
pyramid structure containing global-level information and many pieces of
local-level information from different scales. The feature pyramids of all the
pedestrian images from the same video are fused to form the final
multi-direction and multi-scale feature representation. Experimental results on
two challenging video-based benchmarks, MARS and iLIDS-VID, show the proposed
PiT achieves state-of-the-art performance. Extensive ablation studies
demonstrate the superiority of the proposed pyramid structure. The code is
available at https://git.openi.org.cn/zangxh/PiT.git.
