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|Title:||Multi-Task Structure-Aware Context Modeling for Robust Keypoint-Based Object Tracking|
|Citation:||IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019; 41(4):915-927|
|Xi Li, Liming Zhao, Wei Ji, Yiming Wu, Fei Wu, Ming-Hsuan Yang, Dacheng Tao, Ian Reid|
|Abstract:||In 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.|
|Keywords:||Keypoint tracking; context modeling; structure learning; multi-task learning; metric learning|
|Rights:||© 2018 IEEE|
|Appears in Collections:||Australian Institute for Machine Learning publications|
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