Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/116528
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Type: | Journal article |
Title: | Multi-Task Structure-Aware Context Modeling for Robust Keypoint-Based Object Tracking |
Author: | Li, X. Zhao, L. Ji, W. Wu, Y. Wu, F. Yang, M. Tao, D. Reid, I. |
Citation: | IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019; 41(4):915-927 |
Publisher: | IEEE |
Issue Date: | 2019 |
ISSN: | 0162-8828 2160-9292 |
Statement of Responsibility: | 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 |
DOI: | 10.1109/TPAMI.2018.2818132 |
Published version: | http://dx.doi.org/10.1109/tpami.2018.2818132 |
Appears in Collections: | Aurora harvest 3 Australian Institute for Machine Learning publications Computer Science publications |
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