Public Security Surveillance System Using Blockchain Technology and Advanced Image Processing Techniques

National security is a top priority to mitigate intrusions and criminal acts. Governments require robust national surveillance system that can cover all geographical areas, including the blind spots that may hold violence and criminal incidents' triggers i.e. malls, stadiums, airports, and other key sites. Integrating existing surveillance infrastructures rather than creating centralized solutions will have great potential on scalability as well as providing more liberal framework that is not run by a single point of control. However, this definitely requires establishing secure communication and mutual trust amongst these entities, which is a real challenge. Towards this end, we propose an efficient smart surveillance architecture that combines machine learning and Blockchain technologies to facilitate the exchange of relevant surveillance events as admitted transactions into a permissioned Hyperledger fabric Blockchain. We conducted comprehensive analysis to demonstrate the feasibility of blockchain and the efficiency of the machine learning-based face recognition and matching for real-time surveillance of suspects using heterogeneous surveillance infrastructure. The proposed architecture proved scalability and real-time behavior after putting the system through multiple test cases. With very high matching accuracy, and end-to-end latency of less than 12.8 seconds, the system proves to be scalable, and fast enough for a smart surveillance use case.