Massive surveillance data processing with supercomputing cluster

• We propose a new model of online surveillance data streams processing on a supercomputer.
• Massive parallelization enables online object detection in high-resolution video streams.
• Crowding detection in multi-camera image sequences is performed in nearly real-time.
• Online privacy protection in surveillance systems is possible on cluster architecture.

In recent years, increasingly complex algorithms for automated analysis of surveillance data are being developed. The rapid growth in the number of monitoring installations and higher expectations of the quality parameters of the captured data result in an enormous computational cost of analyzing the massive volume of data. In this paper a new model of online processing of surveillance data streams is proposed, which assumes the use of services running within a supercomputer platform. The study presents some of the highly parallelized algorithms for detecting safety-threatening events in high-resolution-video streams, which were developed during the research, and discusses their performance on the supercomputer platform.