Neuro-heuristic analysis of surveillance video in a centralized IoT system

Security systems are based on the monitoring of specific areas of the facility. The cameras record the selected place for the whole day. Unfortunately, it is difficult to automatically analyze the recorded situations mainly through manual analysis. In this paper, we propose an innovative automatic monitoring data analysis system. To minimize the amount of processed data, a heuristic-based method is proposed to analyze frames. Heuristic algorithm is adapted to image analysis. If the algorithm detects significant changes in pixel values, the frame is sent further to the convolutional neural network. The proposed solution is based on centralized federated learning that allows training a shared model on a local dataset. This guarantees surveillance recordings' privacy with a shared model. The proposal is a hybrid solution that was presented as a mathematical model, tested and compared with other known solutions. Based on conducted experiments, the proposed image processing system minimizes the number of calculations by the hybrid approach that can be valuable for IoT applications. The proposed solution is more effective than the existing solution due to the use of classifiers for the analysis of single frames.