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Convolutional Featu...
Convolutional Features for Correlation Filter Based Visual Tracking
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- Danelljan, Martin (författare)
- Linköpings universitet,Tekniska fakulteten,Datorseende
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- Häger, Gustav (författare)
- Linköpings universitet,Tekniska fakulteten,Datorseende
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- Khan, Fahad Shahbaz (författare)
- Linköpings universitet,Tekniska fakulteten,Datorseende
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- Felsberg, Michael (författare)
- Linköpings universitet,Tekniska fakulteten,Datorseende
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(creator_code:org_t)
- IEEE conference proceedings, 2015
- 2015
- Engelska.
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Ingår i: 2015 IEEE International Conference on Computer Vision Workshop (ICCVW). - : IEEE conference proceedings. - 9781467397117 - 9781467397100 ; , s. 621-629
- Relaterad länk:
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https://liu.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Visual object tracking is a challenging computer vision problem with numerous real-world applications. This paper investigates the impact of convolutional features for the visual tracking problem. We propose to use activations from the convolutional layer of a CNN in discriminative correlation filter based tracking frameworks. These activations have several advantages compared to the standard deep features (fully connected layers). Firstly, they mitigate the need of task specific fine-tuning. Secondly, they contain structural information crucial for the tracking problem. Lastly, these activations have low dimensionality. We perform comprehensive experiments on three benchmark datasets: OTB, ALOV300++ and the recently introduced VOT2015. Surprisingly, different to image classification, our results suggest that activations from the first layer provide superior tracking performance compared to the deeper layers. Our results further show that the convolutional features provide improved results compared to standard handcrafted features. Finally, results comparable to state-of-theart trackers are obtained on all three benchmark datasets.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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