Video Surveillance Processing Algorithms utilizing Artificial Intelligent (AI) for Unmanned Autonomous Vehicles (UAVs)

• Main Authors: Minh T. Nguyen, Linh H. Truong, Trang T.H. Le
• Format: Article
• Language: English
• Published: Elsevier 2021-01-01
• Series: MethodsX
• Subjects: Artificial Intelligence; Background Modeling; Region of Interest (RoI); Convolution Neural Networks (CNN); Video Surveillance (VS); Unmanned aerial vehicles (UAVs)
• Online Access: http://www.sciencedirect.com/science/article/pii/S221501612100265X

With the advancement of science and technology, the combination of the unmanned aerial vehicle (UAV) and camera surveillance systems (CSS) is currently a promising solution for practical applications related to security and surveillance operations. However, one of the biggest risks and challenges for the UAV-CSS is analysis, process, and transmission data, especially, the limitations of computational capacity, storage and overloading the transmission bandwidth. Regard to conventional methods, almost the data collected from UAVs is processed and transmitted that cost huge energy. A certain amount of data is redundant and not necessary to be processed or transmitted. This paper proposes an efficient algorithm to optimize the transmission and reception of data in UAV-CSS systems, based on the platforms of artificial intelligence (AI) for data processing. The algorithm creates an initial background frame and update to the complete background which is sent to server. It splits the region of interest (moving objects) in the scene and then sends only the changes. This supports the CSS to reduce significantly either data storage or data transmission. In addition, the complexity of the systems could be significantly reduced. The main contributions of the algorithm can be listed as follows; - The developed solution can reduce data transmission significantly. - The solution can empower smart manufacturing via camera surveillance. - Simulation results have validated practical viability of this approach.The experimental method results show that reducing up to 80% of storage capacity and transmission data.