The study of elastic streaming processing of multidimensional optical signals in a distributed computing environment

Current trends in increasing the intelligence level of software systems for analyzing data flows under the constraints of processing time require the study of new technologies that take advantage of a distributed computing environment. In this paper, we implement a technology for distributed streaming processing of multidimensional optical signals based on the Apache Flink framework. The technology is studied on the task of transport video surveillance tracking unique objects captured by a system of geographically dispersed video cameras. We study the ability of proposed solution scale the processing depending on the amount of resources in the cloud, the quantity and quality of optical signals. The characteristics of processing processes of a set of frames of varying complexity on a computing cluster are investigated. NVIDIA AI City Challenge is used as test data sets.