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\subsection{Processing}  Real-time analysis is not an explicit goal of this technology as its intended use is primarily for research. However, performance is a serious consideration if, for no other reason than to ensures ensure  that processing remains affordable and doesn't fall behind data collection. In any case, some tasks benefit from as much prior data as possible, particularly machine learning tasks.  In the current iteration of the software, and with today's multi-core prossesors, tasks are highly parralelisable. Feature tracking and trajectory analysis can be performed on multiple video sequences, typically cut up into 20 minute or 1 hour segments, in parralel on a single mid-to-high-performance machine. With parralel processing, processing of video sequences,  memory becomes the main bottleneck. Most bottleneck; 32 GB or more  of the analysis memory  is conducted highly recommended  on a pair multi-core machine. Alternativly, the large majority  of dedicated consumer-grade high-performance machines. calculation tasks can be parralelised at observation level as they are independant events.