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Object Re-IDentification (ReID), one of the most significant problems inbiometrics and surveillance systems, has been extensively studied by imageprocessing and computer vision communities in the past decades. Learning arobust and discriminative feature representation is a crucial challenge forobject ReID. The problem is even more challenging in ReID based on UnmannedAerial Vehicle (UAV) as the images are characterized by continuously varyingcamera parameters (e.g., view angle, altitude, etc.) of a flying drone. Toaddress this challenge, multiscale feature representation has been consideredto characterize images captured from UAV flying at different altitudes. In thiswork, we propose a multitask learning approach, which employs a new multiscalearchitecture without convolution, Pyramid Vision Transformer (PVT), as thebackbone for UAV-based object ReID. By uncertainty modeling of intraclassvariations, our proposed model can be jointly optimized using bothuncertainty-aware object ID and camera ID information. Experimental results arereported on PRAI and VRAI, two ReID data sets from aerial surveillance, toverify the effectiveness of our proposed approach

Uncertainty Aware Multitask Pyramid Vision Transformer For UAV-Based Object Re-Identification