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On the Optimization of Advanced DCF-Trackers [chapter]

Joakim Johnander, Goutam Bhat, Martin Danelljan, Fahad Shahbaz Khan, Michael Felsberg
2019 Lecture Notes in Computer Science  
DCF-based trackers interleave learning of the target detector and target state inference based on this detector.  ...  Trackers based on discriminative correlation filters (DCF) have recently seen widespread success and in this work we dive into their numerical core.  ...  The trackers are analyzed based on their performance on the two benchmarks.  ... 
doi:10.1007/978-3-030-11009-3_2 fatcat:g35swvgdard2diug6eh7pniepy

Discriminative Correlation Filter with Channel and Spatial Reliability

Alan Lukezic, Tomas Vojir, Luka Cehovin Zajc, Jiri Matas, Matej Kristan
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
The CSR-DCF runs in real-time on a CPU.  ...  Experimentally, with only two simple standard features, HoGs and Colornames, the novel CSR-DCF method -DCF with Channel and Spatial Reliability -achieves state-of-the-art results on VOT 2016, VOT 2015  ...  RokŽitko for discussion on complex differentiation.  ... 
doi:10.1109/cvpr.2017.515 dblp:conf/cvpr/LukezicVZMK17 fatcat:bdekfbarvbh25nyevluqnrpi6u

Mutation Sensitive Correlation Filter for Real-Time UAV Tracking with Adaptive Hybrid Label [article]

Guangze Zheng, Changhong Fu, Junjie Ye, Fuling Lin, Fangqiang Ding
2021 arXiv   pre-print
The results indicate that the performance of MSCF tracker surpasses other 26 state-of-the-art DCF-based and deep-based trackers.  ...  However, prevalent discriminative correlation filter (DCF) based trackers are insensitive to target mutations due to a predefined label, which concentrates on merely the centre of the training region.  ...  11 STATE-OF-THE-ART DEEP-BASED TRACKERS ON UAVDT BENCHMARK.  ... 
arXiv:2106.08073v1 fatcat:asmrq6yyardsjegh2ia2qynijq

DAL – A Deep Depth-aware Long-term Tracker [article]

Yanlin Qian and Alan Lukežič and Matej Kristan and Joni-Kristian Kämäräinen and Jiri Matas
2019 arXiv   pre-print
The best RGBD trackers provide high accuracy but are slow to run. On the other hand, the best RGB trackers are fast but clearly inferior on the RGBD datasets.  ...  Comprehensive evaluations show that the proposed tracker achieves state-of-the-art performance on the Princeton RGBD, STC, and the newly-released CDTB benchmarks and runs 20 fps.  ...  The most advanced siamese-based tracker is SiamRPN++ [23] , utilizing ResNet-50 for feature representation. Recently, DCF tends to be merged into an end-to-end deep network.  ... 
arXiv:1912.00660v1 fatcat:4lnrun5kirfy3ga4wo4m7cykqa

Discriminative Correlation Filter Tracker with Channel and Spatial Reliability

Alan Lukežič, Tomáš Vojíř, Luka Čehovin Zajc, Jiří Matas, Matej Kristan
2018 International Journal of Computer Vision  
The CSR-DCF runs in real-time on a CPU.  ...  Experimentally, with only two simple standard features, HoGs and Colornames, the novel CSR-DCF method -- DCF with Channel and Spatial Reliability -- achieves state-of-the-art results on VOT 2016, VOT 2015  ...  Roǩ Zitko for discussion on complex differentiation.  ... 
doi:10.1007/s11263-017-1061-3 fatcat:mnd7fzfwhvgefk7hahffwritae

Visual Object Tracking with Discriminative Filters and Siamese Networks: A Survey and Outlook [article]

Sajid Javed, Martin Danelljan, Fahad Shahbaz Khan, Muhammad Haris Khan, Michael Felsberg, Jiri Matas
2021 arXiv   pre-print
Following the rapid evolution of visual object tracking in the last decade, this survey presents a systematic and thorough review of more than 90 DCFs and Siamese trackers, based on results in nine tracking  ...  Furthermore, we thoroughly analyze the performance of DCF and Siamese trackers on nine benchmarks, covering different experimental aspects of visual tracking: datasets, evaluation metrics, performance,  ...  The The core formulation of both DCF and Siamese trackers only tracking methods were categorized into: based on the point or addresses how to estimate the translation of the target object  ... 
arXiv:2112.02838v1 fatcat:nsre4b5uafeopjb37go6c3obwu

FLOWPROPHET: Generic and Accurate Traffic Prediction for Data-Parallel Cluster Computing

Hao Wang, Li Chen, Kai Chen, Ziyang Li, Yiming Zhang, Haibing Guan, Zhengwei Qi, Dongsheng Li, Yanhui Geng
2015 2015 IEEE 35th International Conference on Distributed Computing Systems  
To this end, we analyze and summarize the common features of popular DCFs, and gain a key insight: since application logic in DCFs is naturally expressed by directed acyclic graphs (DAG), DAG contains  ...  With accurate prediction from FLOWPROPHET, the job completion time of a Hadoop TeraSort benchmark is reduced by 12.52% on our cluster with a simple network scheduler.  ...  The job completion time of a Hadoop TeraSort-25G benchmark is reduced by 12.52% on our 37-server cluster with a simple scheduler cooperating with FLOWPROPHET. VII. ACKNOWLEDGMENTS  ... 
doi:10.1109/icdcs.2015.43 dblp:conf/icdcs/WangCCLZGQLG15 fatcat:p6zoctv6o5gydpk6hdi6k36wqm

Learning Spatial–Temporal Background-Aware Based Tracking

Peiting Gu, Peizhong Liu, Jianhua Deng, Zhi Chen
2021 Applied Sciences  
Considerable experiments on multiple well-known benchmarks show the proposed algorithm is performs favorably against many state-of-the-art trackers and achieves an AUC score of 64.4% on OTB-100.  ...  At the same time, the accuracy of target recognition is improved.  ...  The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  ... 
doi:10.3390/app11188427 fatcat:ecwy6lflg5bkppm2byyanuoquy

Adaptive Weight Collaborative Complementary Learning for Robust Visual Tracking

2019 KSII Transactions on Internet and Information Systems  
Abundant experiments demonstrate that our tracker performs superiorly against several advanced algorithms on both the OTB2013 and OTB2015 datasets while maintaining the high frame rates.  ...  Discriminative correlation filter (DCF) based tracking algorithms have recently shown impressive performance on benchmark datasets.  ...  To perform better on online object tracking task, many recent advancements in DCF based trackers are driven by the use of non-linear kernels [10] , multi-dimensional features [11] , robust scale estimation  ... 
doi:10.3837/tiis.2019.01.018 fatcat:yenwet23ije65nxwxjpgro277q

Equivalence of Correlation Filter and Convolution Filter in Visual Tracking [article]

Shuiwang Li, Qijun Zhao, Ziliang Feng, Li Lu
2021 arXiv   pre-print
Correlation filter-based trackers consider visual tracking as a problem of matching the feature template of the object and candidate regions in the detection sample, in which correlation filter provides  ...  , under the condition that the optimal solutions exist and the ideal filter response is Gaussian and centrosymmetric.  ...  Below we provide a brief review on DCF-based trackers. Bolme et al.  ... 
arXiv:2105.00158v2 fatcat:22gmwuncvnectjpx26gsu46k3m

Correlation Tracking via Spatial-Temporal Constraints and Structured Sparse Regularization

Dan Tian, Shouyu Zang, Binbin Tu
2021 IEEE Access  
Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.  ...  Many advanced tracking methods based on DCF have been developed in recent years. Zheng et al.  ...  Comparative evaluations are performed on the OTB benchmark. The experimental results validate the robustness and effectiveness of our tracker against some state-of-the-art DCF trackers.  ... 
doi:10.1109/access.2021.3086821 fatcat:nrjp7qxlirazzltbjouq7tqdaq

ECO: Efficient Convolution Operators for Tracking

Martin Danelljan, Goutam Bhat, Fahad Shahbaz Khan, Michael Felsberg
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
In recent years, Discriminative Correlation Filter (DCF) based methods have significantly advanced the state-of-theart in tracking.  ...  We revisit the core DCF formulation and introduce: (i) a factorized convolution operator, which drastically reduces the number of parameters in the model; (ii) a compact generative model of the training  ...  Training set size: State-of-the-art DCF trackers, including C-COT, require a large training sample set to be stored due to their reliance on iterative optimization algorithms.  ... 
doi:10.1109/cvpr.2017.733 dblp:conf/cvpr/DanelljanBKF17 fatcat:xznuxj5ywvetredzuevesjwmvi

Learning Aberrance Repressed Correlation Filters for Real-Time UAV Tracking [article]

Ziyuan Huang, Changhong Fu, Yiming Li, Fuling Lin, Peng Lu
2019 arXiv   pre-print
frames to verify the performance of the ARCF tracker and it has proven itself to have outperformed other 20 state-of-the-art trackers based on DCF and deep-based frameworks with sufficient speed for real-time  ...  Traditional framework of discriminative correlation filters (DCF) is often subject to undesired boundary effects.  ...  We believe, with our proposed aberrance repression method, DCF framework and the performances of DCF based trackers can be further improved. Figure 2 . 2 Main work-flow of the proposed ARCF tracker.  ... 
arXiv:1908.02231v2 fatcat:erfhbuqu7zauxf6rxskciebqfa

Online Learning of Discriminative Correlation Filter Bank for Visual Tracking

Jian Wei, Feng Liu
2018 Information  
In this setting, discriminative correlation filter (DCF)-based trackers have demonstrated excellent performance in terms of speed.  ...  Experimental results on the quantitative and qualitative evaluations on the challenging benchmark sequences show that the proposed framework improves tracking performance compared with several state-of-the-art  ...  Conflicts of Interest: The authors declare no conflict of interest. Information 2018, 9, 61  ... 
doi:10.3390/info9030061 fatcat:wq5dxqcjgfewlknfhzebhhhp7q

ECO: Efficient Convolution Operators for Tracking [article]

Martin Danelljan, Goutam Bhat, Fahad Shahbaz Khan, Michael Felsberg
2017 arXiv   pre-print
In recent years, Discriminative Correlation Filter (DCF) based methods have significantly advanced the state-of-the-art in tracking.  ...  We revisit the core DCF formulation and introduce: (i) a factorized convolution operator, which drastically reduces the number of parameters in the model; (ii) a compact generative model of the training  ...  Training set size: State-of-the-art DCF trackers, including C-COT, require a large training sample set to be stored due to their reliance on iterative optimization algorithms.  ... 
arXiv:1611.09224v2 fatcat:abgzsuj3t5avzeau2d5nomvpfq
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