Beyond Sharing Weights in Decoupling Feature Learning Network for UAV
  RGB-Infrared Vehicle Re-Identification
Xingyue Liu Jiahao Qi Chen Chen Kangcheng Bin Ping Zhong
Owing to the capacity of performing full-time target search, cross-modality
vehicle re-identification (Re-ID) based on unmanned aerial vehicle (UAV) is
gaining more attention in both video surveillance and public security. However,
this promising and innovative research has not been studied sufficiently due to
the data inadequacy issue. Meanwhile, the cross-modality discrepancy and
orientation discrepancy challenges further aggravate the difficulty of this
task. To this end, we pioneer a cross-modality vehicle Re-ID benchmark named
UAV Cross-Modality Vehicle Re-ID (UCM-VeID), containing 753 identities with
16015 RGB and 13913 infrared images. Moreover, to meet cross-modality
discrepancy and orientation discrepancy challenges, we present a hybrid weights
decoupling network (HWDNet) to learn the shared discriminative
orientation-invariant features. For the first challenge, we proposed a hybrid
weights siamese network with a well-designed weight restrainer and its
corresponding objective function to learn both modality-specific and modality
shared information. In terms of the second challenge, three effective
decoupling structures with two pretext tasks are investigated to learn
orientation-invariant feature. Comprehensive experiments are carried out to
validate the effectiveness of the proposed method. The dataset and codes will
be released at https://github.com/moonstarL/UAV-CM-VeID.