Abstract
Learning discriminative spatio-temporal representation is the key for solving video re-identification (re-id) challenges. Most existing methods focus on learning appearance features and/or selecting image frames, but ignore optimising the compatibility and interaction of appearance and motion attentive information. To address this limitation, we propose a novel model to learning Spatio-Temporal Associative Representation (STAR). We design local frame-level spatio-temporal association to learn discriminative attentive appearance and short-term motion features, and global video-level spatio-temporal association to form compact and discriminative holistic video representation. We further introduce a pyramid ranking regulariser for facilitating end-to-end model optimisation. Extensive experiments demonstrate the superiority of STAR against state-of-the-art methods on four video re-id benchmarks, including MARS, DukeMTMC-VideoReID, iLIDS-VID and PRID-2011.