A cluster-based computing infrastructure for wide-area multi-modal surveillance networks

Wide-area surveillance networks of diverse sensors present unique challenges for processing the massive amount of data generated. This paper presents a general, flexible computing infrastructure based on free and open-source software components to tackle the problem of quasi-realtime processing of time-sensitive surveillance data. The integration of NIST's SmartFlow system enables the transport of data to a Rocks-based cluster of compnters for more CPU-intensive computations. Services, metadata, and events are made available to the network via a central server called the Blackboard. This allows application programmers to access the raw sensor data within a user-settable time-to-live. A general processing framework based on Trolltech's Qt4 signal and slot mechanism hides the complexity of multi-threaded programming and enables users to fully exploit the potential of the SMP machines in the cluster. Case studies are presented for an independent agent detector that uses multiple cameras as well as scalability results and for the processing of massively multi-channel audio data from a microphone array to achieve e.g. sound-source location in near-realtime.