article

Object tracking: A survey

Authors:

Mubarak ShahAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 38, Issue 4

Pages 13 - es

https://doi.org/10.1145/1177352.1177355

Published: 25 December 2006 Publication History

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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Index Terms

Object tracking: A survey
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Tracking
      2. Computer vision tasks
        Scene understanding

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 38, Issue 4

2006

153 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/1177352

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Copyright © 2006 ACM.

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Publication History

Published: 25 December 2006

Published in CSUR Volume 38, Issue 4

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Cited By

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https://doi.org/10.36306/konjes.1434797
Zhao JKong LLv J(2025)An Overview of Deep Neural Networks for Few-Shot LearningBig Data Mining and Analytics10.26599/BDMA.2024.90200498:1(145-188)Online publication date: Feb-2025
https://doi.org/10.26599/BDMA.2024.9020049
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https://doi.org/10.1109/TR.2024.3392931
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