Li, X.Zhao, L.Ji, W.Wu, Y.Wu, F.Yang, M.Tao, D.Reid, I.2018-12-032018-12-032019IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019; 41(4):915-9270162-88282160-9292http://hdl.handle.net/2440/116528In 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.en© 2018 IEEEKeypoint tracking; context modeling; structure learning; multi-task learning; metric learningJournal article003010245410.1109/TPAMI.2018.28181320004605835000102-s2.0-85044253167447301Reid, I. [0000-0001-7790-6423]