Object tracking: A survey
Abbreviated Journal Title
ACM Comput. Surv.
algorithms; appearance models; contour evolution; feature selection; object detection; object representation; point tracking; shape tracking; REAL-TIME TRACKING; HUMAN MOTION; IMAGE SEGMENTATION; VISUAL TRACKING; HYPOTHESIS TRACKING; APPEARANCE MODELS; TARGET TRACKING; ACTIVE; CONTOURS; MOVING-OBJECTS; OPTICAL-FLOW; Computer Science, Theory & Methods
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.
Acm Computing Surveys
"Object tracking: A survey" (2006). Faculty Bibliography 2000s. 6738.