  Part-level representations are essential for robust person re-identification.
However, common errors that arise during pedestrian detection frequently result
in severe misalignment problems for body parts, which degrade the quality of
part representations. Accordingly, to deal with this problem, we propose a
novel model named Convolutional Deformable Part Models (CDPM). CDPM works by
decoupling the complex part alignment procedure into two easier steps: first, a
vertical alignment step detects each body part in the vertical direction, with
the help of a multi-task learning model; second, a horizontal refinement step
based on attention suppresses the background information around each detected
body part. Since these two steps are performed orthogonally and sequentially,
the difficulty of part alignment is significantly reduced. In the testing
stage, CDPM is able to accurately align flexible body parts without any need
for outside information. Extensive experimental results demonstrate the
effectiveness of the proposed CDPM for part alignment. Most impressively, CDPM
achieves state-of-the-art performance on three large-scale datasets:
Market-1501, DukeMTMC-ReID,and CUHK03.
