Affiliation:
1. Ohio State University
2. ObjectVideo, Inc., Reston, VA
3. University of Central Florida
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.
Publisher
Association for Computing Machinery (ACM)
Subject
General Computer Science,Theoretical Computer Science
Reference160 articles.
1. Human Motion Analysis: A Review
2. Support Vector Tracking
3. Baddeley A. 1992. Errors in binary images and an l version of the haus- dorff metric. Nieuw Archief voor Wiskunde 10 157--183.]] Baddeley A. 1992. Errors in binary images and an l version of the haus- dorff metric. Nieuw Archief voor Wiskunde 10 157--183.]]
4. Ballard D. and Brown C. 1982. Computer Vision. Prentice-Hall.]] Ballard D. and Brown C. 1982. Computer Vision. Prentice-Hall.]]
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