Summary: A Real-Time System for High-Level Video Representation:
Application to Video Surveillance
Aishy Amera, Eric Duboisb, and Amar Mitichec
aConcordia University, Electrical and Computer Engineering, MontrŽeal, QuŽebec, Canada
bSchool of Information Technology and Engineering, University of Ottawa, Ottawa, Canada
cINRS-TŽelŽecommunications, UniversitŽe du QuŽebec, MontrŽeal, QuŽebec, Canada
The steadily increasing need for video content accessibility necessitates the development of stable systems to
represent video sequences based on their high-level (semantic) content. The core of such systems is the automatic
extraction of video content. In this paper, a computational layered framework to effectively extract multiple
high-level features of a video shot is presented. The objective with this framework is to extract rich high-level
video descriptions of real world scenes.
In our framework, high-level descriptions are related to moving objects which are represented by their
spatio-temporal low-level features. High-level features are represented by generic high-level object features such
as events. To achieve higher applicability, descriptions are extracted independently of the video context.
Our framework is based on four interacting video processing layers: enhancement to estimate and reduce
noise, stabilization to compensate for global changes, analysis to extract meaningful objects, and interpretation
to extract context-independent semantic features. The effectiveness and real-time response of the our framework
are demonstrated by extensive experimentation on indoor and outdoor video shots in the presence of multi-object
occlusion, noise, and artifacts.