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Object tracking: A survey

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Yilmaz, Alper, Javed, Omar, Shah, Mubarak·2006·ACM Computing Surveys
DOI 10.1145/1177352.1177355
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Abstract

The goal of this article is to review the state-of-the-art tracking methods, classify them into different categories, and identify new trends. Object tracking, in general, is a challenging problem. Difficulties in tracking objects can arise due to abrupt object motion, changing appearance patterns of both the object and the scene, nonrigid object structures, object-to-object and object-to-scene occlusions, and camera motion. Tracking is usually performed in the context of higher-level applications that require the location and/or shape of the object in every frame. Typically, assumptions are made to constrain the tracking problem in the context of a particular application. In this survey, we categorize the tracking methods on the basis of the object and motion representations used, provide detailed descriptions of representative methods in each category, and examine their pros and cons. Moreover, we discuss the important issues related to tracking including the use of appropriate image features, selection of motion models, and detection of objects.

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Cited by

Metadata

Authors
Yilmaz, A., Javed, O., & Shah, M.
Venue
ACM Computing Surveys
Publisher
Association for Computing Machinery (ACM)
Published
2006-12-25
Volume
38
Issue
4
Pages
13
ISSN
  • 0360-0300 (ISSN-L)
  • 1557-7341
DOI
10.1145/1177352.1177355
OpenAlex
W1995903777
Cited by
4,792
References
8
FWCI
138.40
Type
journal article
Language
en
Open access
green
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.