For the 9000 train accidents reported each year in the European Union [1], the Recording Strip (RS) and Filling-Card
(FC) related to the train activities represent the only usable evidence for SNCF (the French railway operator) and most of
National authorities. More precisely, the RS contains information about the train journey, speed and related Driving
Events (DE) such as emergency brakes, while the FC gives details on the departure/arrival stations. In this context, a
complete checking for 100% of the RS was recently voted by French law enforcement authorities (instead of the 5%
currently performed), which raised the question of an automated and efficient inspection of this huge amount of
recordings. To do so, we propose a machine vision prototype, constituted with cassettes receiving RS and FC to be
digitized. Then, a video analysis module firstly determines the type of RS among eight possible types; time/speed curves
are secondly extracted to estimate the covered distance, speed and stops, while associated DE are finally detected using
convolution process. A detailed evaluation on 15 RS (8000 kilometers and 7000 DE) shows very good results (100% of
good detections for the type of band, only 0.28% of non detections for the DE). An exhaustive evaluation on a panel of
about 100 RS constitutes the perspectives of the work.
KEYWORDS: Video, Databases, Received signal strength, Sensors, Image processing, Data storage, RGB color model, Distributed computing, Sensor networks, Video surveillance
Today's technologies in video analysis use state of the art systems and formalisms like onthologies and datawarehousing
to handle huge amount of data generated from low-level descriptors to high-level descriptors. In the IST
CARETAKER project we develop a multi-dimensional database with distributed features to add a centric data
view of the scene shared between all the sensors of a network.
We propose to enhance possibilities of this kind of system by delegating the intelligence to a lot of other
entities, also known as "Agents" which are specialized little applications, able to walk across the network and
work on dedicated sets of data related to their core domain. In other words, we can reduce, or enhance, the
complexity of the analysis by adding or not feature specific agents, and processing is limited to the data concerned
by the processing.
This article explains how to design and develop an agent oriented systems which can be used by a video
analysis datawarehousing. We also describe how this methodology can distribute the intelligence over the system,
and how the system can be extended to obtain a self reasoning architecture using cooperative agents. We will
demonstrate this approach.
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