PFINDER REAL-TIME TRACKING OF THE HUMAN BODY PDF

It runs at 10Hz on a standard SGI Indy computer, and has performed reliably on thousands of people in many different physical locations. It runs at 10Hz on a standard SGI Indy computer, and has performed reliably on thousands of people in many di erent physical locations. The system uses a multi-class statistical model of color and shape to obtain a 2-D representation of head and hands in a wide range of viewing conditions. P nder has been successfully used in a wide range of applications including wireless interfaces, video databases, and low-bandwidth coding. Applications such as video databases, wireless virtual reality interfaces, smart rooms, very-low-bandwidth video compression, and security monitoring all have in common the need to track and interpret human behavior. Use of image-to-image registration techniques 1, 10] as a preprocessing step allow P nder to function in the presence of camera rotation and zoom, but real-time performance cannot be achieved without special-purpose hardware.

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It runs at 10Hz on a standard SGI Indy computer, and has performed reliably on thousands of people in many different physical locations. The system uses a multiclass statistical model of color and shape to obtain a 2D representation of head and hands in a wide range of viewing conditions.

Pfinder has been successfully used in a wide range of applications including wireless interfaces, video databases, and low-bandwidth coding. Use of image-to-image registration techniques [1], [10], as a preprocessing step, allow Pfinder to function in the presence of camera rotation and zoom, but real-time performance cannot be achieved without special-purpose hardware. The system provides interactive performance on general-purpose hardware, has been tested on thousands of people in several installations around the world, and has performed quite reliably.

The notion of grouping atomic parts of a scene together to form blobPfinder adopts a Maximum A Posteriori Probability MAP aplike entities based on proximity and visual appearance is a natural proach to detection and tracking of the human body using simple one, and has been of interest to visual scientists since the Gestalt psy2D models. It incorporates a priori knowledge about people prichologists studied grouping criteria early in this century [6]. Pfinder is a descendant the boundaries, or contours, of these regions rather than the reof the vision routines originally developed for the ALIVE system gions themselves.

Pfinder is a able and difficult to find and use. Pentland and Kauth et al. Recommended for description method is, in fact, a special case of recent Minimum acceptance by J. Description Length MDL algorithms [5], [16]. However, in contrast to Pfinder, these other systems all require accurate initialization and use local image features. Consequently, they have difficulty with occlusion and require massive computational resources.

Functionally, our systems are perhaps most closely related to the work of Bichsel [3] and Baumberg and Hogg [2]. These systems segment the person from the background in real time using only a standard workstation. Consequently, they cannot track head and hands, determine body pose, or recognize any but the simplest gestures. In the following sections, we will then describe the model-building, and error recovery processes.

Clusters of 2D points have 2D spatial means and covariance matrices, which we shall denote and K. The blob spatial statistics are described in terms of their second-order properties; for computational convenience we will interpret this as a Gaussian model: PrbOg.

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