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SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks [article]

Bo Li, Wei Wu, Qiang Wang, Fangyi Zhang, Junliang Xing, Junjie Yan
2018 arXiv   pre-print
However, Siamese trackers still have accuracy gap compared with state-of-the-art algorithms and they cannot take advantage of feature from deep networks, such as ResNet-50 or deeper.  ...  Siamese network based trackers formulate tracking as convolutional feature cross-correlation between target template and searching region.  ...  Siamese Tracking with Very Deep Networks The most important finding of this work is that the performance of the Siamese network based tracking algorithm can be significantly boosted if it is armed with  ... 
arXiv:1812.11703v1 fatcat:lugxugdp5rhixpz3peuem5cqsa

An Anchor-Free Siamese Network with Multi-Template Update for Object Tracking

Tongtong Yuan, Wenzhu Yang, Qian Li, Yuxia Wang
2021 Electronics  
Inspired by the Siamese network and anchor-free idea, an anchor-free Siamese network (AFSN) with multi-template updates for object tracking is proposed.  ...  of GOT-10k (Generic Object Tracking Benchmark).  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/electronics10091067 fatcat:s7glzec5kjbjtlsockynm3a7ci

Siamese Visual Tracking with Residual Fusion Learning

Xinglong Sun, Guangliang Han, Lihong Guo
2021 IEEE Access  
Multi-stage feature fusion is pretty effective for deep Siamese trackers to promote tracking performance.  ...  In addition, the fusion module is generally optimized along with Siamese network module, which may result in the performance degradation of the whole tracker.  ...  Sun et al.: Siamese Visual Tracking with Residual Fusion Learningbetween two objects with the proposed fusion framework.V.  ... 
doi:10.1109/access.2021.3134066 fatcat:b3xdu2bumjakdd7m6tglsiripu

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  ...  Discriminative Correlation Filters (DCFs) and deep Siamese Networks (SNs) have emerged as dominating tracking paradigms, which have led to significant progress.  ...  Yan, “Siamrpn++: & Business Media, 2006. Evolution of siamese visual tracking with very deep networks,” in [106] Y. Qi, S. Zhang, L. Qin, H. Yao, Q. Huang, J.  ... 
arXiv:2112.02838v1 fatcat:nsre4b5uafeopjb37go6c3obwu

Tracking Holistic Object Representations [article]

Axel Sauer, Elie Aljalbout, Sami Haddadin
2019 arXiv   pre-print
Recent advances in visual tracking are based on siamese feature extractors and template matching.  ...  We propose a framework that is designed to be used on top of previous trackers without any need for further training of the siamese network.  ...  We gratefully acknowledge the general support of Microsoft Germany and the Alfried Krupp von Bohlen und Halbach Foundation.  ... 
arXiv:1907.12920v2 fatcat:32a7xlkd55c2ziipfdhgs67f7i

Cooling-Shrinking Attack: Blinding the Tracker with Imperceptible Noises [article]

Bin Yan and Dong Wang and Huchuan Lu and Xiaoyun Yang
2020 arXiv   pre-print
This feature facilitates to understand neural networks deeply and to improve the robustness of deep learning models.  ...  predicted bounding box to shrink, making the tracked target invisible to trackers.  ...  of modern deep neural networks [28, 10] .  ... 
arXiv:2003.09595v1 fatcat:xy33cpybene75ks5nwc5eewho4

Cooling-Shrinking Attack: Blinding the Tracker With Imperceptible Noises

Bin Yan, Dong Wang, Huchuan Lu, Xiaoyun Yang
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
This feature facilitates to understand neural networks deeply and to improve the robustness of deep learning models.  ...  predicted bounding box to shrink, making the tracked target invisible to trackers.  ...  of modern deep neural networks [28, 10] .  ... 
doi:10.1109/cvpr42600.2020.00107 dblp:conf/cvpr/YanWLY20 fatcat:hez2mapqnrft3grnv5mhonbh3q

Fast Online Object Tracking and Segmentation: A Unifying Approach [article]

Qiang Wang, Li Zhang, Luca Bertinetto, Weiming Hu, Philip H.S. Torr
2019 arXiv   pre-print
Our method, dubbed SiamMask, improves the offline training procedure of popular fully-convolutional Siamese approaches for object tracking by augmenting their loss with a binary segmentation task.  ...  In this paper we illustrate how to perform both visual object tracking and semi-supervised video object segmentation, in real-time, with a single simple approach.  ...  We would also like to acknowledge the support of the Royal Academy of Engineering and FiveAI Ltd. Qiang Wang is partly supported by the NSFC (Grant No. 61751212, 61721004 and U1636218).  ... 
arXiv:1812.05050v2 fatcat:qyss4k7ksnhoxekybf3ctkkvb4

Fast Online Object Tracking and Segmentation: A Unifying Approach

Qiang Wang, Li Zhang, Luca Bertinetto, Weiming Hu, Philip H.S. Torr
2019 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
Our method, dubbed SiamMask, improves the offline training procedure of popular fully-convolutional Siamese approaches for object tracking by augmenting their loss with a binary segmentation task.  ...  In this paper we illustrate how to perform both visual object tracking and semi-supervised video object segmentation, in real-time, with a single simple approach.  ...  We would also like to acknowledge the support of the Royal Academy of Engineering and FiveAI Ltd. Qiang Wang is partly supported by the NSFC (Grant No. 61751212, 61721004 and U1636218).  ... 
doi:10.1109/cvpr.2019.00142 dblp:conf/cvpr/Wang0BHT19 fatcat:uuftbjelrnezxcrw4jtazmec7e

The Seventh Visual Object Tracking VOT2019 Challenge Results

Matej Kristan, Amanda Berg, Linyu Zheng, Litu Rout, Luc Van Gool, Luca Bertinetto, Martin Danelljan, Matteo Dunnhofer, Meng Ni, Min Young Kim, Ming Tang, Ming-Hsuan Yang (+169 others)
2019 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)  
The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative.  ...  The VOT toolkit has been updated to support both standard shortterm, long-term tracking and tracking with multi-channel imagery.  ...  The SiamM Ds tracker is a modified version of SiamMask [96] to track objects in RGB and Depth images. SiamMask produces segmentation on the tracking target.  ... 
doi:10.1109/iccvw.2019.00276 dblp:conf/iccvw/KristanBZRGBDDN19 fatcat:ogwxim7cgjanxiwq7dddqs66gy

TRAT: Tracking by Attention Using Spatio-Temporal Features [article]

Hasan Saribas, Hakan Cevikalp, Okan Köpüklü, Bedirhan Uzun
2020 arXiv   pre-print
Robust object tracking requires knowledge of tracked objects' appearance, motion and their evolution over time.  ...  In this paper, we propose a two-stream deep neural network tracker that uses both spatial and temporal features.  ...  [5] introduced a very fast CF based method using the minimum output of squared error (MOSSE) for visual tracking.  ... 
arXiv:2011.09524v1 fatcat:u32znmfjrzae5dmj7rtbjqzon4

SiamMask: A Framework for Fast Online Object Tracking and Segmentation [article]

Weiming Hu, Qiang Wang, Li Zhang, Luca Bertinetto, Philip H.S. Torr
2022 arXiv   pre-print
We improve the offline training procedure of popular fully-convolutional Siamese approaches by augmenting their losses with a binary segmentation task.  ...  In this paper we introduce SiamMask, a framework to perform both visual object tracking and video object segmentation, in real-time, with the same simple method.  ...  In particular, evolutions of the fully-convolutional Siamese approach [23] considerably improved tracking performance by making use of region proposals [24] , hard negative mining [25] , ensembling  ... 
arXiv:2207.02088v1 fatcat:csvfeqdb55e3nmlg2i4p4iv254

Real-Time Siamese Multiple Object Tracker with Enhanced Proposals [article]

Lorenzo Vaquero, Víctor M. Brea, Manuel Mucientes
2022 arXiv   pre-print
To solve the aforementioned problems and allow the tracking of dozens of arbitrary objects in real-time, we propose SiamMOTION.  ...  Thus, motion estimation systems are often employed, which either do not scale well with the number of targets or produce features with limited semantic information.  ...  In order to expand the current trend of visual object trackers for motion estimation we propose SiamMOTION (Siamese Multiple Object Tracker with Inertia and at-tentiOn Network).  ... 
arXiv:2202.04966v1 fatcat:7iyn6uhfknh43iv47l7xh7675i

Inflated 3D ConvNet context analysis for violence detection

David Freire-Obregón, Paola Barra, Modesto Castrillón-Santana, Maria De Marsico
2021 Machine Vision and Applications  
This amount of information can be hardly managed by humans.  ...  Most of those proposals consider a pre-processing step to only focus on some regions of interest in the scene, i.e., those actually containing a human subject.  ...  Acknowledgements This work is partially funded by the ULPGC under Project ULPGC2018-08, the Spanish Ministry of Economy and Competitiveness (MINECO) under Project RTI2018-093337-B-I00 and the Gobierno  ... 
doi:10.1007/s00138-021-01264-9 fatcat:27xnk5tbyvakpjbaxvceq4owjm

Multi-domain Collaborative Feature Representation for Robust Visual Object Tracking [article]

Jiqing Zhang and Kai Zhao and Bo Dong and Yingkai Fu and Yuxin Wang and Xin Yang and Baocai Yin
2021 arXiv   pre-print
some challenging conditions, and a Unique Extractor for RGB (UER) based on Deep Convolutional Neural Networks to extract texture and semantic information in RGB domain.  ...  We show our approach outperforms all compared state-of-the-art tracking algorithms and verify event-based data is a powerful cue for tracking in challenging scenes.  ...  .: Siamrpn++: Evolution of siamese visual tracking with very deep networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2019) 29.  ... 
arXiv:2108.04521v2 fatcat:eefdlvkfm5g6djtyrnvwa7dtyy
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