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Filters
End-to-end representation learning for Correlation Filter based tracking
[article]
2017
arXiv
pre-print
Preserved Fulltext
This enables learning deep features that are tightly coupled to the Correlation Filter. ...
Previous works that use the Correlation Filter, however, have adopted features that were either manually designed or trained for a different task. ...
Multi-channel Correlation Filter There is little advantage to the dual solution when training a single-channel Correlation Filter from the circular shifts of a single base example. ...
arXiv:1704.06036v1
fatcat:ccabjldtwzfsfm644cp6qzanpy
End-to-End Representation Learning for Correlation Filter Based Tracking
2017
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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This enables learning deep features that are tightly coupled to the Correlation Filter. ...
Previous works that use the Correlation Filter, however, have adopted features that were either manually designed or trained for a different task. ...
Multi-channel Correlation Filter There is little advantage to the dual solution when training a single-channel Correlation Filter from the circular shifts of a single base example. ...
doi:10.1109/cvpr.2017.531
dblp:conf/cvpr/ValmadreBHVT17
fatcat:xrcp5d56xbbs5m5fohxy4opqlq
Do not Lose the Details: Reinforced Representation Learning for High Performance Visual Tracking
2018
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
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This work presents a novel end-to-end trainable CNN model for high performance visual object tracking. ...
Moreover, the correlation filter layer working on the fine-grained representations leverages a global context constraint for accurate object appearance modeling. ...
Conclusions We have proposed an end-to-end encoder-decoder network for the CF based tracking. ...
doi:10.24963/ijcai.2018/137
dblp:conf/ijcai/WangZXGHM18
fatcat:vuixvii3hfdw5pp5snso2kxaom
Robust Visual Tracking via Collaborative and Reinforced Convolutional Feature Learning
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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In the paper, we design an end-to-end trainable tracking framework based on Siamese network, which proposes to learn the low-level fine-grained and high-level semantic representations simultaneously with ...
The low-level features are exploited and updated with a correlation filter layer for adaptive tracking and the high-level features are compared through crosscorrelation directly for robust tracking. ...
end-to-end to find the features most suitable for the correlation filter. ...
doi:10.1109/cvprw.2019.00085
dblp:conf/cvpr/LiKWL19
fatcat:mwzpnvf7w5fjxflrnoxcqnp4h4
Learning Reinforced Attentional Representation for End-to-End Visual Tracking
[article]
2019
arXiv
pre-print
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In this paper, we propose an end-to-end network model to learn reinforced attentional representation for accurate target object discrimination and localization. ...
Moreover, we incorporate a contextual attentional correlation filter into the backbone network to make our model be trained in an end-to-end fashion. ...
Motivated by above observations, we aim to achieve high-performance visual tracking by learning efficient representation and correlation filters mutually in an end-to-end network. ...
arXiv:1908.10009v2
fatcat:yr5ikhrzdjdz7bt2toxqzfrkeq
Kernalised Multi-resolution Convnet for Visual Tracking
[article]
2017
arXiv
pre-print
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Moreover, the transfered multi-reslution CNN renders it possible to be integrated into the RNN temporal learning framework, therefore opening the door on the end-to-end temporal deep learning (TDL) for ...
Visual tracking is intrinsically a temporal problem. Discriminative Correlation Filters (DCF) have demonstrated excellent performance for high-speed generic visual object tracking. ...
Adaptive learning rate Traditional correlation based filter updates the model by a fixed parameter η. ...
arXiv:1708.00577v1
fatcat:u6kz2j3yxrck5c2hlogdeqx4dy
Kernalised Multi-resolution Convnet for Visual Tracking
2017
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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Moreover, the transfered multireslution CNN renders it possible to be integrated into the RNN temporal learning framework, therefore opening the door on the end-to-end temporal deep learning (TDL) for ...
Visual tracking is intrinsically a temporal problem. Discriminative Correlation Filters (DCF) have demonstrated excellent performance for high-speed generic visual object tracking. ...
Adaptive learning rate Traditional correlation based filter updates the model by a fixed parameter η. ...
doi:10.1109/cvprw.2017.278
dblp:conf/cvpr/WuZLZ17
fatcat:st46eaueuvf4xmmxhwyr2yoha4
Learning Attentions: Residual Attentional Siamese Network for High Performance Online Visual Tracking
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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the separation of representation learning and discriminator learning. ...
The RASNet model reformulates the correlation filter within a Siamese tracking framework, and introduces different kinds of the attention mechanisms to adapt the model without updating the model online ...
to constrain correlation filter learning. ...
doi:10.1109/cvpr.2018.00510
dblp:conf/cvpr/WangTXGHM18
fatcat:5f2p5jhp55dzxigay5tqhidvza
Comparative Study of ECO and CFNet Trackers in Noisy Environment
[article]
2018
arXiv
pre-print
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Recent deep learning based trackers have shown good performance on various tracking challenges. ...
We aim to study the robustness of two state of the art trackers in the presence of noise including Efficient Convolutional Operators (ECO) and Correlation Filter Network (CFNet). ...
End to End Representation Learning For Correlation Filter Based Tracking In end to end representation learning for correlation filter based tracking [18] , is a Siamese based tracking algorithm where ...
arXiv:1801.09360v1
fatcat:wkc7kfsbdnbnja2xzcpcx6scwq
End-to-End Flow Correlation Tracking with Spatial-Temporal Attention
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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To the best of our knowledge, this is the first work to jointly train flow and tracking task in deep learning framework. ...
Discriminative correlation filters (DCF) with deep convolutional features have achieved favorable performance in recent tracking benchmarks. ...
It is worth noting that CFNet [37] and DCFNet [40] interpret the correlation filters as a differentiable layer in a Siamese tracking framework, thus achieving an end-to-end representation learning. ...
doi:10.1109/cvpr.2018.00064
dblp:conf/cvpr/ZhuWZY18
fatcat:rkxgrswoebbzzkhbvaiyfsiy34
CREST: Convolutional Residual Learning for Visual Tracking
2017
2017 IEEE International Conference on Computer Vision (ICCV)
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Discriminative correlation filters (DCFs) have been shown to perform superiorly in visual tracking. ...
Our method integrates feature extraction, response map generation as well as model update into the neural networks for an end-to-end training. ...
Tracking by Correlation Filters. Correlation filters for visual tracking have attracted considerable attention due to the computational efficiency in the Fourier domain. ...
doi:10.1109/iccv.2017.279
dblp:conf/iccv/SongMGZL017
fatcat:f5v3wttxenhanl56b6dhgzijgm
CREST: Convolutional Residual Learning for Visual Tracking
[article]
2017
arXiv
pre-print
Preserved Fulltext
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Discriminative correlation filters (DCFs) have been shown to perform superiorly in visual tracking. ...
Our method integrates feature extraction, response map generation as well as model update into the neural networks for an end-to-end training. ...
Tracking by Correlation Filters. Correlation filters for visual tracking have attracted considerable attention due to the computational efficiency in the Fourier domain. ...
arXiv:1708.00225v1
fatcat:425ey5aq6vbzbiu6l254vvgeya
RSINet: Rotation-Scale Invariant Network for Online Visual Tracking
[article]
2020
arXiv
pre-print
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in an end-to-end manner. ...
Most Siamese network-based trackers perform the tracking process without model update, and cannot learn targetspecific variation adaptively. ...
The ECO [4] and ASRCF [42] are the SOTA correlation filter-based trackers integrating both hand-crafted and deep features for target representation. ...
arXiv:2011.09153v1
fatcat:ep4n7o7dcvhz3p42t3fewl73aa
Efficient Scale Estimation Methods using Lightweight Deep Convolutional Neural Networks for Visual Tracking
[article]
2020
arXiv
pre-print
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In recent years, visual tracking methods that are based on discriminative correlation filters (DCF) have been very promising. ...
The proposed methods are formulated based on either holistic or region representation of convolutional feature maps to efficiently integrate into DCF formulations to learn a robust scale model in the frequency ...
The proposed methods using lightweight CNN models learn a 2D scale correlation filter according to the holistic or region-based representation of convolutional feature maps. ...
arXiv:2004.02933v2
fatcat:p3sqisjjpvhbrcyzceknelefnm
Saliency-Associated Object Tracking
[article]
2021
arXiv
pre-print
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Most existing trackers based on deep learning perform tracking in a holistic strategy, which aims to learn deep representations of the whole target for localizing the target. ...
Further, we design a saliency-association modeling module to associate the captured saliencies together to learn effective correlation representations between the exemplar and the search image for state ...
End-to-end parameter learning. The whole model SAOT is trained in an end-to-end manner. ...
arXiv:2108.03637v1
fatcat:wxonbt2lzngclmyvdy37sqpoau
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