Recently, because of its importance in computer vision and surveillance systems, object tracking has progressed rapidly over the last two decades. Researches on such systems still face several theoretical and technical problems that badly impact not only the accuracy of position measurements but also the continuity of tracking. In this paper, a novel strategy for tracking multiple objects using static cameras is introduced, which can be used to grant a cheap, easy installation and robust tracking system. The proposed tracking strategy is based on scenes captured by a number of static video cameras. Each camera is attached to a workstation that analyzes its stream. All workstations are connected directly to the tracking server, which harmonizes the system, collects the data, and creates the output spatial-tempo database. Our contribution comes in two issues. The first is to present a new methodology for transforming the image coordinates of an object to its real coordinates. The second is to offer a flexible event-based object tracking strategy. The proposed tracking strategy has been tested over a CAD of soccer game environment. Preliminary experimental results show the robust performance of the proposed tracking strategy. 1. Introduction Because of the advance in surveillance systems, object tracking has been an active research topic in the computer vision community over the last two decades as it is an essential prerequisite for analyzing and understanding video data. However, tracking an object, in general, is a challenging problem. Difficulties in tracking objects may arise due to several reasons such as abrupt object motion, changing appearance patterns of the object and/or the scene, nonrigid object structures, partial/full object-to-object and object-to-scene occlusions, camera motion, loss of information caused by projection of the 3D world on a 2D image, noise in images, complex object motion, complex object shapes, and real-time processing requirements. Moreover, tracking is usually performed in the context of higher-level applications, which in turn require the location and/or shape of the object in every captured frame. Accordingly, several assumptions should be considered to constrain the tracking problem for a particular application. A great deal of interest in the field of object tracking has been generated due to (i) the recent evolution of high-speed computers, (ii) the availability of high quality and inexpensive sensors (video cameras), and (iii) the increasing demand for an automated real-time video analysis. Tracking an object is to
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