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231225s2019 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2018.2818132
|2 doi
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|a eng
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| 100 |
1 |
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|a Li, Xi
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a Multi-Task Structure-Aware Context Modeling for Robust Keypoint-Based Object Tracking
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| 264 |
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|c 2019
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| 336 |
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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| 500 |
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|a Date Revised 20.11.2019
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a 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
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| 650 |
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4 |
|a Journal Article
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| 700 |
1 |
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|a Zhao, Liming
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ji, Wei
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wu, Yiming
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wu, Fei
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yang, Ming-Hsuan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Tao, Dacheng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Reid, Ian
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 41(2019), 4 vom: 09. Apr., Seite 915-927
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
1 |
8 |
|g volume:41
|g year:2019
|g number:4
|g day:09
|g month:04
|g pages:915-927
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| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TPAMI.2018.2818132
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