Siamese Instance Search for Tracking
Open accessAbstract
In this paper we present a tracker, which is radically different from state-of-the-art trackers: we apply no model updating, no occlusion detection, no combination of trackers, no geometric matching, and still deliver state-of-the-art tracking performance, as demonstrated on the popular online tracking benchmark (OTB) and six very challenging YouTube videos. The presented tracker simply matches the initial patch of the target in the first frame with candidates in a new frame and returns the most similar patch by a learned matching function. The strength of the matching function comes from being extensively trained generically, i.e., without any data of the target, using a Siamese deep neural network, which we design for tracking. Once learned, the matching function is used as is, without any adapting, to track previously unseen targets. It turns out that the learned matching function is so powerful that a simple tracker built upon it, coined Siamese INstance search Tracker, SINT, which only uses the original observation of the target from the first frame, suffices to reach state-of-the-art performance. Further, we show the proposed tracker even allows for target re-identification after the target was absent for a complete video shot.
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Previous models have to improve nonlinear representation ability by introducing fashion structures, using additional modules, or designing effective online updaters.
“Thus, previous models have to improve the nonlinear representation ability by introducing fashion structures [21, 44, 48], using additional modules [7, 46, 13], designing effective online updaters [2, 47, 10], to name a few.”
From Transformer Tracking (2021) · cites this paper as [7]
Cites
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“The most popular paradigm is based on matching local image descriptors between the query and the candidate image [40, 36, 20, 37, 45, 43] and is especially accurate for buildings [36].”
BackgroundHypercolumns for object segmentation and fine-grained localization
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Fully-Convolutional Siamese Networks for Object Tracking
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SiamRPN++: Evolution of Siamese Visual Tracking With Very Deep Networks
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“…to provide extensive baselines, comprising deep trackers ( e.g ., MDNet [42], TRACA [5], CFNet [50], SiamFC [4], StructSiam [59], DSiam [16], SINT [49] and VITAL [48]), correlation fil-ter trackers with hand-crafted features ( e.g ., ECO HC [7], DSST [8], CN [11], CSK [19], KCF [20], fDSST [9],…”
BackgroundChen, X., Yan, B., Zhu, J., Wang, D., Yang, X., & Lu, H. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00803 · Cited by 1,588 · see citing works
“In recent years, Siamese-based methods have been more popular in the tracking field [37, 1, 22, 42, 21, 44, 48].”
MethodLearning to Track at 100 FPS with Deep Regression Networks
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D3S – A Discriminative Single Shot Segmentation Tracker
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GlobalTrack: A Simple and Strong Baseline for Long-Term Tracking
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- Authors
- Tao, R., Gavves, E., & Smeulders, A. W. M.
- ORCID
- Gavves, Efstratios
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 1420-1429
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2408241409
- Cited by
- 1,208
- References
- 8
- Citations by year
- 2026: 12 · 2025: 57 · 2024: 94 · 2023: 124 · 2022: 166 · 2021: 162 · 2020: 184 · 2019: 208 · 2018: 122 · 2017: 75 · 2016: 4
- FWCI
- 47.29
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- UvA-DARE (University of Amsterdam) (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- arXiv (Cornell University)
- 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 › Advanced Image and Video Retrieval Techniques
- Keywords
- object tracking, Siamese network, instance search, patch matching, target re-identification
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