Perceptual data mining : bootstrapping visual intelligence from tracking behavior

One common characteristic of all intelligent life is continuous perceptual input. A decade ago, simply recording and storing a a few minutes of full frame-rate NTSC video required special hardware. Today, an inexpensive personal computer can process video in real-time tracking and recording information about multiple objects for extended periods of time, which fundamentally enables this research. This thesis is about Perceptual Data Mining (PDM), the primary goal of which is to create a real-time, autonomous perception system that can be introduced into a wide variety of environments and, through experience, learn to model the activity in that environment. The PDM framework infers as much as possible about the presence, type, identity, location, appearance, and activity of each active object in an environment from multiple video sources, without explicit supervision. PDM is a bottom-up, data-driven approach that is built on a novel, robust attention mechanism that reliably detects moving objects in a wide variety of environments. A correspondence system tracks objects through time and across multiple sensors producing sets of observations of objects that correspond to the same object in extended environments. Using a co-occurrence modeling technique that exploits the variation exhibited by objects as they move through the environment, the types of objects, the activities that objects perform, and the appearance of specific classes of objects are modeled. Different applications of this technique are demonstrated along with a discussion of the corresponding issues.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2002

Author dc:creator

• Stauffer, Christopher P. (Christopher Paul), 1971-

Advisor dc:contributor.advisor

• W.E.L. Grimson.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/8111
• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Identifier URI

http://dspace.mit.edu/handle/1721.1/8111

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/8111