Tracking process captures the state of an object. The state of an object is defined in terms of its dynamic and static properties such as location, speed, color, temperature, size, etc. The set of dynamic and static properties for tracking very much depends on the agency who wants to track. For example, police needs different set of properties to tracks people than to track a vehicle than the air force. The tracking scenario also affects the selection of parameters. Tracking is done by a system referred to in this paper as "Tracker." It is a system that consists of a set of input devices such as sensors and a set of algorithms that process the data captured by these input devices. The process of tracking has three distinct steps (a) object discovery, (b) identification of discovered object, and (c) object introduction to the input devices. In this paper we focus mainly on the object discovery part with a brief discussion on introduction and identification parts. We develops a formal tracking framework (model) called "Discover, Identify, and Introduce Model (DIIM)" for building efficient tracking systems. Our approach is heuristic and uses reasoning leading to learning to develop a knowledge base for object discovery. We also develop a tracker for the Air Force system called N-CET.

Date Published: 5 May 2011
PDF: 10 pages
Proc. SPIE 8050, Signal Processing, Sensor Fusion, and Target Recognition XX, 805016 (5 May 2011); doi: 10.1117/12.882178

Published in SPIE Proceedings Vol. 8050:
Signal Processing, Sensor Fusion, and Target Recognition XX
Ivan Kadar, Editor(s)