  One of the major restrictions on the performance of video-based person re-id
is partial noise caused by occlusion, blur and illumination. Since different
spatial regions of a single frame have various quality, and the quality of the
same region also varies across frames in a tracklet, a good way to address the
problem is to effectively aggregate complementary information from all frames
in a sequence, using better regions from other frames to compensate the
influence of an image region with poor quality. To achieve this, we propose a
novel Region-based Quality Estimation Network (RQEN), in which an ingenious
training mechanism enables the effective learning to extract the complementary
region-based information between different frames. Compared with other feature
extraction methods, we achieved comparable results of 92.4%, 76.1% and 77.83%
on the PRID 2011, iLIDS-VID and MARS, respectively. In addition, to alleviate
the lack of clean large-scale person re-id datasets for the community, this
paper also contributes a new high-quality dataset, named "Labeled Pedestrian in
the Wild (LPW)" which contains 7,694 tracklets with over 590,000 images.
Despite its relatively large scale, the annotations also possess high
cleanliness. Moreover, it's more challenging in the following aspects: the age
of characters varies from childhood to elderhood; the postures of people are
diverse, including running and cycling in addition to the normal walking state.

Computer Vision and Pattern Recognition