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Owing to the capacity of performing full-time target search, cross-modalityvehicle re-identification (Re-ID) based on unmanned aerial vehicle (UAV) isgaining more attention in both video surveillance and public security. However,this promising and innovative research has not been studied sufficiently due tothe data inadequacy issue. Meanwhile, the cross-modality discrepancy andorientation discrepancy challenges further aggravate the difficulty of thistask. To this end, we pioneer a cross-modality vehicle Re-ID benchmark namedUAV Cross-Modality Vehicle Re-ID (UCM-VeID), containing 753 identities with16015 RGB and 13913 infrared images. Moreover, to meet cross-modalitydiscrepancy and orientation discrepancy challenges, we present a hybrid weightsdecoupling network (HWDNet) to learn the shared discriminativeorientation-invariant features. For the first challenge, we proposed a hybridweights siamese network with a well-designed weight restrainer and itscorresponding objective function to learn both modality-specific and modalityshared information. In terms of the second challenge, three effectivedecoupling structures with two pretext tasks are investigated to learnorientation-invariant feature. Comprehensive experiments are carried out tovalidate the effectiveness of the proposed method. The dataset and codes willbe released at https://github.com/moonstarL/UAV-CM-VeID.

Beyond Sharing Weights in Decoupling Feature Learning Network for UAV RGB-Infrared Vehicle Re-Identification