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231224s2017 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2016.2551245
|2 doi
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|a eng
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| 100 |
1 |
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|a Bing Wang
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Tracklet Association by Online Target-Specific Metric Learning and Coherent Dynamics Estimation
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|c 2017
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 20.09.2018
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|a Date Revised 20.09.2018
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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 this paper, we present a novel method based on online target-specific metric learning and coherent dynamics estimation for tracklet (track fragment) association by network flow optimization in long-term multi-person tracking. Our proposed framework aims to exploit appearance and motion cues to prevent identity switches during tracking and to recover missed detections. Furthermore, target-specific metrics (appearance cue) and motion dynamics (motion cue) are proposed to be learned and estimated online, i.e., during the tracking process. Our approach is effective even when such cues fail to identify or follow the target due to occlusions or object-to-object interactions. We also propose to learn the weights of these two tracking cues to handle the difficult situations, such as severe occlusions and object-to-object interactions effectively. Our method has been validated on several public datasets and the experimental results show that it outperforms several state-of-the-art tracking methods
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Gang Wang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Kap Luk Chan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li Wang
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 39(2017), 3 vom: 15. März, Seite 589-602
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|x 1939-3539
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|g volume:39
|g year:2017
|g number:3
|g day:15
|g month:03
|g pages:589-602
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