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Proceedings Paper

Automatic Tracking Of Multiple Objects
Author(s): Andrew Bernat; Stephen Riter
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Paper Abstract

We are developing a computer vision system to automatically detect and track human motion across the international border between the United States and Mexico. Fundamental requirements are that the system work in real time under varying environmental conditions with relatively inexpensive hardware. The work we describe is applicable to a wide range of multiple object tracking problems. This paper describes the algorithm we have developed to detect and track moving objects. The algorithm is based on the notion of path coherence. However, the algorithm presented there is not suitable for our application because it requires noise free images, all trajectories must be present from the first through last images, and it requires multiple passes throught the trajectory points as each new image is acquired. This procedure is not acceptable for real time applications. We have previously reported on the front end of our system which takes video images and determines the areas which represent changing objects'. Thus the input to the tracking portion of the system consists of a binary image representing the changing pixels. In this paper we present a detailed description of the tracking algorithm, its implementation in Smalltalk-80, and samples of its operation. We also discuss system performance as a function of trajectory complexity and image noise level.

Paper Details

Date Published: 21 March 1989
PDF: 8 pages
Proc. SPIE 1095, Applications of Artificial Intelligence VII, (21 March 1989); doi: 10.1117/12.969263
Show Author Affiliations
Andrew Bernat, The University of Texas at El Paso (United States)
Stephen Riter, The University of Texas at El Paso (United States)

Published in SPIE Proceedings Vol. 1095:
Applications of Artificial Intelligence VII
Mohan M. Trivedi, Editor(s)

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