Learning patterns of activity using real-time tracking
Stauffer, C.
Grimson, W.E.L.
Artificial Intelligence Lab., MIT, Cambridge, MA ;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Aug 2000
Volume: 22,
Issue: 8
On page(s): 747-757
ISSN: 0162-8828
References Cited: 24
CODEN: ITPIDJ
INSPEC Accession Number: 6744968
Digital Object Identifier: 10.1109/34.868677
Current Version Published: 2002-08-06
Abstract
Our goal is to develop a visual monitoring system that passively
observes moving objects in a site and learns patterns of activity from
those observations. For extended sites, the system will require multiple
cameras. Thus, key elements of the system are motion tracking, camera
coordination, activity classification, and event detection. In this
paper, we focus on motion tracking and show how one can use observed
motion to learn patterns of activity in a site. Motion segmentation is
based on an adaptive background subtraction method that models each
pixel as a mixture of Gaussians and uses an online approximation to
update the model. The Gaussian distributions are then evaluated to
determine which are most likely to result from a background process.
This yields a stable, real-time outdoor tracker that reliably deals with
lighting changes, repetitive motions from clutter, and long-term scene
changes. While a tracking system is unaware of the identity of any
object it tracks, the identity remains the same for the entire tracking
sequence. Our system leverages this information by accumulating joint
co-occurrences of the representations within a sequence. These joint
co-occurrence statistics are then used to create a hierarchical
binary-tree classification of the representations. This method is useful
for classifying sequences, as well as individual instances of activities
in a site
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