Object Tracking: A Survey Alper Yilmaz Ohio State University Omar Javed ObjectVideo, Inc. and Mubarak Shah University of Central Florida The goal of this article is to review the state-of-the-art tracking methods, classify them into different cate- gories, 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. Categories and Subject Descriptors: I.4.8 [Image Processing and Computer Vision]: Scene Analysis��� Tracking General Terms: Algorithms Additional Key Words and Phrases: Appearance models, contour evolution, feature selection, object detection, object representation, point tracking, shape tracking ACM Reference Format: Yilmaz, A., Javed, O., and Shah, M. 2006. Object tracking: A survey. ACM Comput. Surv. 38, 4, Article 13 (Dec. 2006), 45 pages. DOI = 10.1145/1177352.1177355 http://doi.acm.org/10.1145/1177352.1177355 This material is based on work funded in part by the US Government. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the US Government. Author���s address: A. Yilmaz, Department of CEEGS, Ohio State University email: yilmaz.15@osu.edu O. Javed, ObjectVideo, Inc., Reston, VA 20191 email: ojaved@objectvideo.com M. Shah, School of EECS, University of Central Florida email: shah@cs.ucf.edu. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or direct commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is per- mitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requested from Publications Dept., ACM, Inc., 2 Penn Plaza, Suite 701, New York, NY 10121-0701 USA, fax +1 (212) 869-0481, or permissions@acm.org. c 2006 ACM 0360-0300/2006/12-ART13 $5.00 DOI: 10.1145/1177352.1177355 http://doi.acm.org/10.1145/ 1177352.1177355. ACM Computing Surveys, Vol. 38, No. 4, Article 13, Publication date: December 2006.