Communication Mechanisms and Middleware for Distributed Video Surveillance

Abstract

A new generation of advanced surveillance systems is being conceived as a collection of multisensor components such as video, audio, and mobile robots interacting in a cooperating manner to enhance situation awareness capabilities to assist surveillance personnel. The prominent issues that these systems face are the improvement of existing intelligent video surveillance systems, the inclusion of wireless networks, the use of low power sensors, the design architecture, the communication between different components, the fusion of data emerging from different type of sensors, the location of personnel (providers and consumers), and the scalability of the system. This paper focuses on the aspects pertaining to real-time distributed architecture and scalability. For example, to meet real-time requirements, these systems need to process data streams in concurrent environments, designed by taking into account scheduling and synchronization. This paper proposes a framework for the design of visual surveillance systems based on components derived from the principles of real-time networks/data-oriented requirements implementation scheme. It also proposes the implementation of these components using the well-known middleware technology common object request broker architecture. Results using this architecture for video surveillance are presented through an implemented prototype.

A Systematic Review of Intelligence Video Surveillance: Trends, Techniques, Frameworks, and Datasets

“…Furthermore, it also provides timely warnings to alert security personnels. With the development in sensory technology, surveillance cameras, and sound recording systems, it is possible to provide the intelligent video surveillance system integrated with the realtime monitoring of moving objects within a specific environment [54], [98], [111], [176]- [178].…”

The future of video analytics for surveillance and its ethical implications

“…Given the needs of surveillance operators, the complexity of scenes under surveillance and limitations in current video analytics, next generation surveillance demands distributed, network infrastructures, greater robustness and adaptation in their video analytic algorithms and real-time operation. To address these needs a real-time distributed and scalable architecture was built and demonstrated through an implemented prototype (Valera et al (2011)). The prototype included an approach to real-time robust segmentation of people within the monitored surveillance scene (Tweed & Ferryman, 2008) and human action modelling and recognition based on image-based extraction of silhouettes of people (Ragheb et al, 2008;Orrite et al, 2008).…”

Distributed Deep Learning Model for Intelligent Video Surveillance Systems with Edge Computing

“…V IDEO Surveillance (VS) technology has become a fundamental tool for the public and private sector security, such as traffic monitoring, indoor monitoring, and crime and violence detection [1][2][3]. Edge Artificial Intelligence (EAI) is a promising technology that combines Artificial Intelligence (AI), Internet of Things (IoT), and Edge Computing (EC) technologies [4][5][6].…”

“…In the field of VS, existing AI and deep learning algorithms, such as Convolutional Neural Network (CNN) and Deep Neural Network (DNN), are mainly used for static image analysis, rather than image streaming and video analysis [13,14]. Focusing on distributed VS systems and AI algorithms, most current VS systems rely on traditional centralized or cloud-based solutions, facing huge data communication overhead, high latency, and severe packet loss limitations [3,12]. Existing studies have proposed various distributed AI and Deep Learning (DL) algorithms in distributed computing clusters and cloud computing platforms, such as distributed CNN, DNN, and LSTM [9,15,16].…”