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One of the major restrictions on the performance of video-based person re-idis partial noise caused by occlusion, blur and illumination. Since differentspatial regions of a single frame have various quality, and the quality of thesame region also varies across frames in a tracklet, a good way to address theproblem is to effectively aggregate complementary information from all framesin a sequence, using better regions from other frames to compensate theinfluence of an image region with poor quality. To achieve this, we propose anovel Region-based Quality Estimation Network (RQEN), in which an ingenioustraining mechanism enables the effective learning to extract the complementaryregion-based information between different frames. Compared with other featureextraction 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 alleviatethe lack of clean large-scale person re-id datasets for the community, thispaper also contributes a new high-quality dataset, named "Labeled Pedestrian inthe Wild (LPW)" which contains 7,694 tracklets with over 590,000 images.Despite its relatively large scale, the annotations also possess highcleanliness. Moreover, it's more challenging in the following aspects: the ageof characters varies from childhood to elderhood; the postures of people arediverse, including running and cycling in addition to the normal walking state.

Region-based Quality Estimation Network for Large-scale Person Re-identification