A Complex Model of Human Recognition in Emergencies by a Multi-level Drone System

Human recognition in a variety of environments is a hot area of research. The results of such research are already assisting in the analysis and prediction of human behaviour based on motion, colour map video sequences, infrared video, etc. However, the physical presence of humans in such operations is often dangerous due to the inaccessibility of search areas. To solve this problem, a multi-level drone system has been proposed, where each level has its own tasks and capabilities. The system includes four levels: swarm cloud, swarm operator, swarm mate and swarm node. A human recognition model has been proposed as a complex model consisting of a convolutional neural network (CNN) and a deep learning network (LSTM). The sequential use of these networks allows both spatial (CNN) and temporal (LSTM) features to be considered. As a result, a convolutional model with long short-term memory is proposed. The solution to the problem of increased computational complexity caused by the use of multiple neural networks lies in the architectural features of a multi-level drone system. Namely, swarm nodes can exchange heterogeneous information in real time. Such communication makes it possible to narrow down the searched area to that in which the potentially searched object has already been detected. This approach implies a reduction in the dimensionality of the video sequence at the processing stage, which in turn reduces both the resources required and the time spent on recognition. The application of this model is appropriate in systems where recognition time can be sacrificed for accuracy. For instance, in a drone swarm system for search and rescue of disaster victims. The swarm can always return to the area where cloud-level calculations indicate a person is located.