Recently,
one paper on person re-identification is accepted by the journal IEEE TPAMI.
The full name of TPAMI is Transactions on Pattern Analysis and Machine
Intelligence, which is a
CCF-ranked-A
top-tier Artificial Intelligence journal with a high IF score as 17.86. Below
is a brief introduction of this work.
Ruibing
Hou, Bingpeng Ma, Hong Chang, Xinqian Gu, Shiguang Shan and Xilin Chen, “Feature
Completion for Occluded Person Re-Identification”, IEEE Transactions on Pattern
Analysis and Machine Intelligence (TPAMI), 2021. (Accepted)
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. 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. The framework of RFCnet is below.
In
summary, the main contributions of our work lie in four aspects: (1) proposing
to use feature completion to address the occluded person reID problem; (2)
designing a SRFC and TRFC module that respectively captures spatial and
temporal contexts to recover the features of occluded regions; (3) constructing
a large-scale video occluded reID dataset Occluded-DukeMTMCVideoReID to facilitate
the research on occluded reID; (4) achieving superior performance on both
occluded and holistic reID compared with state-of-the-art methods.
Download: