Visual Object Tracking With Discriminative Filters and Siamese Networks: A Survey and Outlook
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00366877" target="_blank" >RIV/68407700:21230/23:00366877 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/TPAMI.2022.3212594" target="_blank" >https://doi.org/10.1109/TPAMI.2022.3212594</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TPAMI.2022.3212594" target="_blank" >10.1109/TPAMI.2022.3212594</a>
Alternative languages
Result language
angličtina
Original language name
Visual Object Tracking With Discriminative Filters and Siamese Networks: A Survey and Outlook
Original language description
Accurate and robust visual object tracking is one of the most challenging and fundamental computer vision problems. It entails estimating the trajectory of the target in an image sequence, given only its initial location, and segmentation, or its rough approximation in the form of a bounding box. Discriminative Correlation Filters (DCFs) and deep Siamese Networks (SNs) have emerged as dominating tracking paradigms, which have led to significant progress. 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 benchmarks. First, we present the background theory of both the DCF and Siamese tracking core formulations. Then, we distinguish and comprehensively review the shared as well as specific open research challenges in both these tracking paradigms. 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, and speed comparisons. We finish the survey by presenting recommendations and suggestions for distinguished open challenges based on our analysis.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Research Center for Informatics</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Transactions on Pattern Analysis and Machine Intelligence
ISSN
0162-8828
e-ISSN
1939-3539
Volume of the periodical
45
Issue of the periodical within the volume
5
Country of publishing house
US - UNITED STATES
Number of pages
23
Pages from-to
6552-6574
UT code for WoS article
000964792800077
EID of the result in the Scopus database
2-s2.0-85139840009