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Object tracking

ACM Computing Surveys · 2006 · Vol. 38(4) · pp. 13–13
Alper YılmazOmar JavedMubarak Shah

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.

Video Surveillance and Tracking MethodsAdvanced Image and Video Retrieval TechniquesRobotics and Sensor-Based LocalizationComputer scienceComputer visionArtificial intelligenceObject (grammar)Video trackingTracking (education)Context (archaeology)CategorizationMotion (physics)Match moving
Citations
4,680
FWCI
111.15
field-weighted impact
References
176
Percentile
100%
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Citations per year
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