|
|
|
|
| LEADER |
01000naa a22002652 4500 |
| 001 |
NLM252946626 |
| 003 |
DE-627 |
| 005 |
20231224165204.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231224s2015 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TIP.2015.2479460
|2 doi
|
| 028 |
5 |
2 |
|a pubmed24n0843.xml
|
| 035 |
|
|
|a (DE-627)NLM252946626
|
| 035 |
|
|
|a (NLM)26390462
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Zhu, Guibo
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Weighted Part Context Learning for Visual Tracking
|
| 264 |
|
1 |
|c 2015
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Completed 03.02.2016
|
| 500 |
|
|
|a Date Revised 27.01.2016
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status PubMed-not-MEDLINE
|
| 520 |
|
|
|a Context information is widely used in computer vision for tracking arbitrary objects. Most of the existing studies focus on how to distinguish the object of interest from background or how to use keypoint-based supporters as their auxiliary information to assist them in tracking. However, in most cases, how to discover and represent both the intrinsic properties inside the object and the surrounding context is still an open problem. In this paper, we propose a unified context learning framework that can effectively capture spatiotemporal relations, prior knowledge, and motion consistency to enhance tracker's performance. The proposed weighted part context tracker (WPCT) consists of an appearance model, an internal relation model, and a context relation model. The appearance model represents the appearances of the object and the parts. The internal relation model utilizes the parts inside the object to directly describe the spatiotemporal structure property, while the context relation model takes advantage of the latent intersection between the object and background regions. Then, the three models are embedded in a max-margin structured learning framework. Furthermore, prior label distribution is added, which can effectively exploit the spatial prior knowledge for learning the classifier and inferring the object state in the tracking process. Meanwhile, we define online update functions to decide when to update WPCT, as well as how to reweight the parts. Extensive experiments and comparisons with the state of the arts demonstrate the effectiveness of the proposed method
|
| 650 |
|
4 |
|a Journal Article
|
| 650 |
|
4 |
|a Research Support, Non-U.S. Gov't
|
| 700 |
1 |
|
|a Wang, Jinqiao
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Zhao, Chaoyang
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Lu, Hanqing
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 24(2015), 12 vom: 18. Dez., Seite 5140-51
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
|
| 773 |
1 |
8 |
|g volume:24
|g year:2015
|g number:12
|g day:18
|g month:12
|g pages:5140-51
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TIP.2015.2479460
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 24
|j 2015
|e 12
|b 18
|c 12
|h 5140-51
|