Surveillance systems are of vital importance for the development of smart cities. These systems can be considered vision organs of such cities. It is expected that a huge amount of data (Big Data) will be generated in smart cities. Therefore, to ensure the safety of its citizens, it is important to provide an efficient and real-time analysis of these data to get real-time responses, when catastrophic events occur. Accordingly, transmitting this massive data to the cloud, to be processed, is relatively slow. Therefore, the purpose of this project is to implement a fog/edge computing-based surveillance system to offer real-time data processing. When surveillance videos capture an incident, the data get transferred to the edge for processing. Moreover, a rapid response is then provided to properly handle the occasion. Furthermore, despite tackling scalability obstacles, the system should handle privacy-sensitive data to overcome the privacy challenges in smart cities.

Our main objective is to build a fog computing based framework for surveillance data in order to detect abnormal human behaviors in the streets. We will improve an algorithm for anomaly detection with remarkable stability and accuracy. The system is also developed to work on enhancing the latency by reducing the amount of data. Moreover, we will work on developing the privacy protection scheme for data security.

• The system will provide real time data processing analysis.

• The system will response immediately and take a proper action when an event is detected.

• The system will provide privacy assurance for the citizens.

• The system will provide scalability with unlimited storage.

• Edge/Fog computing-based surveillance system to monitoring roads, streets, and squares in smart cities.

• Scalability system to handle such massive data generated from surveillance cameras and sensors.

• Efficiently detect any abnormal actions caused by human behavior and response immediately.

• Privacy assurance by blur people’s faces to secure their exposed identity in the video.