Cited by 705Video Surveillance and Tracking MethodsAdvanced Image and Video Retrieval TechniquesVisual Attention and Saliency Detection

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A survey of appearance models in visual object tracking

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Li, Xi, Hu, Weiming, Shen, Chunhua, Zhang, Zhongfei, Dick, Anthony, Hengel, Anton Van Den·2013·ACM Transactions on Intelligent Systems and Technology

DOI 10.1145/2508037.2508039

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Abstract

Visual object tracking is a significant computer vision task which can be applied to many domains, such as visual surveillance, human computer interaction, and video compression. Despite extensive research on this topic, it still suffers from difficulties in handling complex object appearance changes caused by factors such as illumination variation, partial occlusion, shape deformation, and camera motion. Therefore, effective modeling of the 2D appearance of tracked objects is a key issue for the success of a visual tracker. In the literature, researchers have proposed a variety of 2D appearance models. To help readers swiftly learn the recent advances in 2D appearance models for visual object tracking, we contribute this survey, which provides a detailed review of the existing 2D appearance models. In particular, this survey takes a module-based architecture that enables readers to easily grasp the key points of visual object tracking. In this survey, we first decompose the problem of appearance modeling into two different processing stages: visual representation and statistical modeling. Then, different 2D appearance models are categorized and discussed with respect to their composition modules. Finally, we address several issues of interest as well as the remaining challenges for future research on this topic. The contributions of this survey are fourfold. First, we review the literature of visual representations according to their feature-construction mechanisms (i.e., local and global). Second, the existing statistical modeling schemes for tracking-by-detection are reviewed according to their model-construction mechanisms: generative, discriminative, and hybrid generative-discriminative. Third, each type of visual representations or statistical modeling techniques is analyzed and discussed from a theoretical or practical viewpoint. Fourth, the existing benchmark resources (e.g., source codes and video datasets) are examined in this survey.

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Research works printed but not linked: 5

10.5555/794191.794734 · DOI
10.5555/521384.836819 · DOI
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10.5555/1018428.1020821 · DOI
Visentini , I. , Snidaro , L. , and Foresti , G. L . 2008. Dynamic ensemble for target tracking . In Proceedings of the International Workshop on Visual Surveillance. Visentini, I., Snidaro, L., and Foresti, G. L. 2008. Dynamic ensemble for target tracking. In Proceedings of the International Workshop on Visual Surveillance.

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“In this section, we mainly focus on the tracking benchmarks that are relevant to our work, and refer the readers to surveys [32, 47, 54, 30] for tracking algorithms.”
Background
TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild
Müller, M., Bibi, A., Giancola, S., Alsubaihi, S., & Ghanem, B. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01246-5_19 · Cited by 1,192

See all 705

Authors: Li, X., Hu, W., Shen, C., Zhang, Z., Dick, A., & Hengel, A. V. D.
Affiliations: Chinese Academy of Sciences (CN)
The University of Adelaide (AU)
Binghamton University (US)
ORCID: Li, Xi
Hu, Weiming
Shen, Chunhua
Zhang, Zhongfei
Dick, Anthony
Hengel, Anton Van Den
Venue: ACM Transactions on Intelligent Systems and Technology
Publisher: Association for Computing Machinery (ACM)
Published: 2013-09
Volume: 4
Issue: 4
Pages: 1-48
ISSN: 2157-6904 (ISSN-L)
2157-6912
DOI: 10.1145/2508037.2508039
OpenAlex: W1984914017
Cited by: 705
References: 214
Citations by year: 2026: 4 · 2025: 7 · 2024: 15 · 2023: 26 · 2022: 30 · 2021: 50 · 2020: 51 · 2019: 81 · 2018: 98 · 2017: 97 · 2016: 111 · 2015: 83 · 2014: 47 · 2013: 5
FWCI: 92.20
Type: journal article
Language: en
Open access: green
arxiv.org
Licence: www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations: ACM Transactions on Intelligent Systems and Technology (publishedVersion)
arXiv (Cornell University) (submittedVersion)
Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
arXiv (Cornell University)
Funders: Australian Research Council
National Natural Science Foundation of China (60935002)
Ministry of Science and Technology of the People's Republic of China (2012AA012504, 2012CB316400)
Natural Science Foundation of Beijing Municipality (4121003)
Division of Information and Intelligent Systems (IIS-0812114, CCF-1017828)
Division of Computing and Communication Foundations (IIS-0812114, CCF-1017828)
Guangdong Natural Science Foundation (S2012020011081)
Zhejiang Provincial Engineering Center onMedia Data Cloud Processing and Analysis
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 Image and Video Retrieval Techniques
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
Keywords: visual object tracking, visual representation, statistical modeling, tracking-by-detection, generative models, discriminative models, hybrid generative-discriminative models, illumination variation, partial occlusion, shape deformation, camera movement

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A survey of appearance models in visual object tracking

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