Multi-Task Structure-Aware Context Modeling for Robust Keypoint-Based Object Tracking

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dc.contributor.authorLi, X.
dc.contributor.authorZhao, L.
dc.contributor.authorJi, W.
dc.contributor.authorWu, Y.
dc.contributor.authorWu, F.
dc.contributor.authorYang, M.
dc.contributor.authorTao, D.
dc.contributor.authorReid, I.
dc.date.issued2019
dc.description.abstractIn the fields of computer vision and graphics, keypoint-based object tracking is a fundamental and challenging problem, which is typically formulated in a spatio-temporal context modeling framework. However, many existing keypoint trackers are incapable of effectively modeling and balancing the following three aspects in a simultaneous manner: temporal model coherence across frames, spatial model consistency within frames, and discriminative feature construction. To address this problem, we propose a robust keypoint tracker based on spatio-temporal multi-task structured output optimization driven by discriminative metric learning. Consequently, temporal model coherence is characterized by multi-task structured keypoint model learning over several adjacent frames; spatial model consistency is modeled by solving a geometric verification based structured learning problem; discriminative feature construction is enabled by metric learning to ensure the intra-class compactness and inter-class separability. To achieve the goal of effective object tracking, we jointly optimize the above three modules in a spatio-temporal multi-task learning scheme. Furthermore, we incorporate this joint learning scheme into both single-object and multi-object tracking scenarios, resulting in robust tracking results. Experiments over several challenging datasets have justified the effectiveness of our single-object and multi-object trackers against the state-of-the-art.
dc.description.statementofresponsibilityXi Li, Liming Zhao, Wei Ji, Yiming Wu, Fei Wu, Ming-Hsuan Yang, Dacheng Tao, Ian Reid
dc.identifier.citationIEEE Transactions on Pattern Analysis and Machine Intelligence, 2019; 41(4):915-927
dc.identifier.doi10.1109/TPAMI.2018.2818132
dc.identifier.issn0162-8828
dc.identifier.issn2160-9292
dc.identifier.orcidReid, I. [0000-0001-7790-6423]
dc.identifier.urihttp://hdl.handle.net/2440/116528
dc.language.isoen
dc.publisherIEEE
dc.rights© 2018 IEEE
dc.source.urihttps://doi.org/10.1109/tpami.2018.2818132
dc.subjectKeypoint tracking; context modeling; structure learning; multi-task learning; metric learning
dc.titleMulti-Task Structure-Aware Context Modeling for Robust Keypoint-Based Object Tracking
dc.typeJournal article
pubs.publication-statusPublished

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