Activity Recognition
from Video Sequences using Declarative Models
Nathanaël ROTA and Monique THONNAT
Abstract:
We propose here a new approach for video sequence interpretation based
on declarative models of activities. The aim of the video
sequence interpretation is to recognize incrementaly certain situations,
like states of the scene, events and scenarios, in a video stream, in order
to understand what happens in the scene. The input of the activity
recognition is an {\it a priori} model of the scene and human tracked in
it. The activity recognition is composed of two subproblems. First,
end-users have to declare all the activities in a configuration phase.
Secondly, the declared models must be automatically recognized. To
solve the first problem, we propose a homogeneous declarative formalism
to describe all the activities (states of the scene, events and scenarios).
The activities are described by the conditions between the objects of the
scene. To solve the second problem, we translate it into a constraints
satisfaction problem. Then, we use a classical CSP algorithm to recognize
the activities in video sequences. Finally, we present some results to
show the robustness of the approach.
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Dernière mise à jour:
4/10/02
Agnes.Cortell@sophia.inria.fr