Computed·By

By Computed·By

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Paranoid Shelter is a recent installation / architectural device that fabric | ch finalized later in 2011 after a 6 months residency at the EPFL-ECAL Lab in Renens (Switzerland). It was realized with the support of Pro Helvetia, the OFC, the City of Lausanne and the State of Vaud. It was initiated and first presented as sketches back in 2008 (!), in the context of a colloquium about surveillance at the Palais de Tokyo in Paris.

On the first technical drawings and sketches of the Paranoid Shelter project, the entire system was just looking like a (big) mess of wires, sensors and video cameras, all concentrated on a pretty tiny space where humans will have difficulties to move in. The entire space is consciously organised around tracking methods/systems, the space being delimited by 3 [augmented] posts which host a set of sensors, video cameras and microphones. It includes networked [power over ethernet] video cameras, microphones and a set of wireless ambient sensors (giving the ability of measuring temperature, O2 and CO2 gaz concentration, current atmospheric pressure, light, etc...).

Based on a real-time analysis of major sensors hardware, the system is able to control DMX lights, a set of two displays (one LCD screen and one projector) and to produce sound through a dynamically generated text to speech process.

All programs were developed using openFrameworks enhanced by a set of dedicated in-house C++ libraries in order to be able to capture networked camera video flow, control any DMX compatible piece of hardware and collect wireless Libelum sensor's data. Sound analysis programs, LCD display program and the main program are all connected to each other via a local network. The main program is in charge of collecting other program's data, performing the global analysis of the system's activity, recording system's raw information to a database and controlling system's [re]actions (lights, display).

The overall system can act in an [autonomous] way by controlling the entire installation behavior while it can also be remotely controlled when used on stage, in the context of a theater play.

Collecting all sensor's flows is one of the basic task. Cameras are used to track movements, microphones measure sound activity and sensors collect a set of ambient parameters. Even if data capture consists in some basic network based tasks, it is easily raised to upper complexity level when each data collection should occur simultaneously, in real-time, [without,with] a [limited,acceptable] delay. Major raw data analysis have to occur directly after data acquisition in order to minimize the time-shift in the system's space awareness. This first level of data analysis brings out mainly frequencies information, quantity of activity and 2D location tracking (from the point of view of each camera). Every single piece of raw information is systematically recorded in a dedicated database : it reduces system's memory footprint (by keeping it almost constant) without loosing any activity information. From time to time the system can access these recorded information in its post-analysis process, when required, mainly to add a time-scale dimension on the global activity that occurred in the monitored space. Time isolated information can be interpreted in a rough and basic way, while time composition of the same information or a set of information may bring additional meanings by verifying information consistency over time (of course, it could be in a negative or a positive way, by confirming or refuting a first level deduced activity information). Another level of analysis can be reached by taking in account the spacial distribution of sensors in the overall installation. The system is then able to compute 3D information getting an awareness of activities within the space it is monitoring. It generates a second level of data analysis, spatialised, that will increase the global understanding of captured data by the system.

Recorded activities are made available to the [audience,visitors] through a wifi access point. Networked cameras can be accessed in real time, giving the ability to humans to see some of the system's [inputs]. Thus, network activity is also monitored as another sign of human presence, the system can then [detect] activity elsewhere than in its dedicated space.

Whatever how numerous are collected data, the system faces a real problem when it comes to the interpretation of these data while not having benefit of a human brain. Events that are quite obvious to humans, do not mean anything to computers and softwares. In order to avoid the use of some artificial neural networks simulation (which may still be a good option to explore), I have decided to compute a limited set of parameters, all based on previously analysed data, only computed lately when the system may decide to react to perceived activities. It defines a kind of global [mood] of the system, based on which it will [decide] whether to be aggressive (from a human point of view) by making the global tracking activity [noticeable] by humans evolving in the installation's space, or by focusing tracking sensors on a given area or by trying to enhance some sensor's information analysis, whether to settle in a kind of silent mode.

Moreover, the evolution of these parameters are also studied in time, making the [mood] evolving in a human way, increasing and decreasing [analogically]. System's [mood] may be wrong or [unjustified,weird] from a human point of view, but that's where [multi-dimensional] software becomes interesting. Beyond a certain complexity, by adding computation layers on top of each over, having written every single line of code does not allow the programmer to predict precisely what next system's [re]action will be.

We did reach here monitoring system limitations which is obviously [interpretation,comprehension]. As long as automatic system can not correctly [understand] data, humans will need to be in the loop, making all these monitoring systems quite useless [as expert system], except for producing an enormous quantity of data that still need to be post-analysed by a human brain. As the system is producing an important set of heteregeneous data, a set of rules may suggest to the system some sort of data correlation. These rules should not be too [tights,precises] in order to avoid producing obvious system's interpretation, while keeping them slightly [out of focus] may allow [smart,astonishing] conclusion being produced. So there's rooms here for additional implementation of the data analysis processes that can still completely change the way the entire installation [can,may] behave.