LRDNN: Local-refining based Deep Neural Network for Person Re-Identification
with Attribute Discerning
Abstract
Recently, pose or attribute information has been
widely used to solve person re-identification (reID) problem. However, the inaccurate output from
pose or attribute modules will impair the final person re-ID performance. Since re-ID, pose estimation and attribute recognition are all based
on the person appearance information, we propose a Local-refining based Deep Neural Network
(LRDNN) to aggregate pose estimation and attribute recognition to improve the re-ID performance. To this end, we add a pose branch to
extract the local spatial information and optimize
the whole network on both person identity and attribute objectives. To diminish the negative affect
from unstable pose estimation, a novel structure
called channel parse block (CPB) is introduced to
learn weights on different feature channels in pose
branch. Then two branches are combined with
compact bilinear pooling. Experimental results on
Market1501 and DukeMTMC-reid datasets illustrate the effectiveness of the proposed method