  Person re-identification (reID) plays an important role in computer vision.
However, existing methods suffer from performance degradation in occluded
scenes. In this work, we propose an occlusion-robust block, Region Feature
Completion (RFC), for occluded reID. Different from most previous works that
discard the occluded regions, RFC block can recover the semantics of occluded
regions in feature space. Firstly, a Spatial RFC (SRFC) module is developed.
SRFC exploits the long-range spatial contexts from non-occluded regions to
predict the features of occluded regions. The unit-wise prediction task leads
to an encoder/decoder architecture, where the region-encoder models the
correlation between non-occluded and occluded region, and the region-decoder
utilizes the spatial correlation to recover occluded region features. Secondly,
we introduce Temporal RFC (TRFC) module which captures the long-term temporal
contexts to refine the prediction of SRFC. RFC block is lightweight, end-to-end
trainable and can be easily plugged into existing CNNs to form RFCnet.
Extensive experiments are conducted on occluded and commonly holistic reID
benchmarks. Our method significantly outperforms existing methods on the
occlusion datasets, while remains top even superior performance on holistic
datasets. The source code is available at
https://github.com/blue-blue272/OccludedReID-RFCnet.
