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Multi target tracki...
Multi target tracking from drones by learning from generalized graph differences
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- Ardo, Hakan (författare)
- Axis communications AB
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- Nilsson, Mikael (författare)
- Lund University,Lunds universitet,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH
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(creator_code:org_t)
- 2020
- 2020
- Engelska 9 s.
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Ingår i: Proceedings - 2019 International Conference on Computer Vision : Workshops, ICCVW 2019 - Workshops, ICCVW 2019. - 9781728150239 - 9781728150246 ; , s. 46-54
- Relaterad länk:
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http://dx.doi.org/10...
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visa fler...
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https://lup.lub.lu.s...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Formulating the multi object tracking problem as a network flow optimization problem is a popular choice. The weights of such network flow problem can be learnt efficiently from training data using a recently introduced concept called Generalized Graph Differences (GGD). This allows a general tracker implementation to be specialized to drone videos by training it on the VisDrone dataset. Two modifications to the original GGD is introduced in this paper and a result with an average precision of 23.09 on the test set of VisDrone 2019 was achieved.
Ämnesord
- NATURVETENSKAP -- Matematik -- Beräkningsmatematik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Computational Mathematics (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
Nyckelord
- Multi target tracking
Publikations- och innehållstyp
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- ref (ämneskategori)
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