Publisher Summary In the surveillance research field applied to public areas, crowd monitoring is very useful but presents particularly complex problems. Recognizing objects and persons, and tracking their movements in complex real scenes by using a sequence of images are among the most difficult tasks in computer vision. Object and human motion tracking in 3D real scenes can be achieved by means of Kahnan filtering—a suitable mathematical model for describing objects and persons—and a refined dynamic model for tracking them while moving and reciprocally interacting is needed. Such approaches can provide accurate and robust results even in uncontrolled real-life working conditions. A method for tracking only a single moving person is presented. The limiting assumption decays, and more general and more complex situations are considered, as several objects moving and interacting in real scenes are treated. It is addressed to present the two main phases as the basis of object recognition and tracking: the selection of a set of image features characterizing each detected mobile object or group of objects, and consequently allowing the system to distinguish an object with respect to another, and the matching procedure which allows one to recognize a certain object even after various frames in which it disappears completely or partially. Because of its real-time functioning, accuracy, and robustness, this method can be used in real-life surveillance systems.
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