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Cited by 274Video Surveillance and Tracking MethodsAdvanced Neural Network ApplicationsVisual Attention and Saliency Detection

VideoMatch: Matching Based Video Object Segmentation

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Hu, Yuan-Ting, Huang, Jia-Bin, Schwing, Alexander G.·2018·Lecture Notes in Computer Science
DOI 10.1007/978-3-030-01237-3_4
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  • Adopting a memory architecture to store object information from past frames is indispensable for accurately tracking objects in video.

    “To address the above challenges, adopting a memory architecture to store the object information from past frames is indispensable for accurately tracking objects in video [5,7,10,29,40,52,56,57,64,66,68].”

    From SAM2LONG: Enhancing SAM 2 for Long Video Segmentation with a Training-Free Memory Tree (2025) · cites this paper as [29]

    Section 3.3 (p. 7) says the method appends features from processed frames to its foreground and background feature sets, and the ablation study (Section 4.4, p. 13) reports a modest accuracy gain from those updates. But the same ablation reports 0.792 mIoU without either update, so it does not support calling storage of information from past frames indispensable. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 45

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Cited by

Sorted by citations

Authors
Hu, Y.-T., Huang, J.-B., & Schwing, A. G.
Affiliations
  • University of Illinois Urbana-Champaign (US)
  • Virginia Tech (US)
Corresponding author
Hu, Yuan-Ting
ORCID
Venue
Lecture Notes in Computer Science
Publisher
Springer International Publishing
Published
2018
Pages
56-73
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-030-01237-3_4
OpenAlex
W2889658408
Cited by
274
References
9
Citations by year
2026: 5 · 2025: 20 · 2024: 33 · 2023: 39 · 2022: 46 · 2021: 47 · 2020: 56 · 2019: 27 · 2018: 1
FWCI
9.87
Type
conference paper
Language
en
Open access
Licence
Locations
  • Lecture notes in computer science (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • National Science Foundation (1755785, 1718221)
  • Nvidia
  • Samsung
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
Keywords
video object segmentation, template matching, feature matching, template-based segmentation, real-time segmentation, computational efficiency

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