IP-based Surveillance Research Articles

The Security of IP-Based Video Surveillance Systems.

Over the last decade, video surveillance systems have become a part of the Internet of Things (IoT). These IP-based surveillance systems now protect industrial facilities, railways, gas stations, and even one’s own home. Unfortunately, like other IoT systems, there are inherent security risks which can lead to significant violations of a user’s privacy. In this review, we explore the attack surface of modern surveillance systems and enumerate the various ways they can be compromised with real examples. We also identify the threat agents, their attack goals, attack vectors, and the resulting consequences of successful attacks. Finally, we present current countermeasures and best practices and discuss the threat horizon. The purpose of this review is to provide researchers and engineers with a better understanding of a modern surveillance systems’ security, to harden existing systems and develop improved security solutions.

An SDN-based framework for improving the performance of underprovisioned IP Video Surveillance networks

In the coming years, the dominance of video in global Internet traffic is expected to intensify due to the ongoing migration from analog CCTV to IP-based surveillance systems. As opposed to analog, IP cameras can be integrated into pre-existing computer networks and thus, are more cost-effective. However, since video is bandwidth-intensive, contention among the streams from multiple IP cameras and traffic from other applications results to a severely degraded network performance. Software-Defined Networking (SDN) is a relatively new paradigm which aims to build dynamically configurable networks. The decoupling of forwarding and control functions in SDN architecture enables a centralized controller to create a map of the network topology by utilizing the information collected from the switches. This paper proposes an SDN-based framework to enhance the performance of IP Video Surveillance (IPVS) systems deployed over underprovisioned networks. As a means of allowing the controller to infer a stream's QoS metrics and to execute bitrate adjustment or rerouting as necessary, two video quality indicators were also formulated by utilizing the statistics messages available in OpenFlow, a widely recognized protocol in SDN. Moreover, our design is built on the idea that the video streams in surveillance systems offer different utilities depending on the captured event. Experimental results show that employing the proposed framework improved the video streams' overall packet loss, latency, jitter and throughput by 88%, 36%, 11% and 5% respectively. The comparison of the video streams' QoS metrics also suggests that the framework is capable of prioritizing the reception of selected streams. Furthermore, we also demonstrate that the proposed framework can be easily extended to handle the case of an IPVS system wherein the streams are subjected to dynamic priority assignment.

Policy control framework-based algorithm for uncertain QoS harmonization in IP-based live video surveillance communications

There are around 15 million surveillance camera installed in US and about 10 % of it is IP based. This number of IP-based surveillance is constantly growing as it gives an opportunity for smart real time surveillance. When the number of camera increases, it create a challenge to smart surveillance both in terms of cost and efficiency. Cost—as to monitor so many views, there is a need to have enough people. Efficiency—due to overload of monitoring, there are chances that important tracking is missed. Intelligent surveillance helps in avoiding many such issues, however, there is a constant threat that due to lack of appropriate policy control framework, tracking may be affected due to inherent network issues or human glitches. The paper presents a novel way, where a policy control framework is deployed for IP-based surveillance which will help in efficient monitoring. The paper studies the QoS pattern where such a system is deployed and shows that the utility function used for such tracking follows a linear uncertainty distribution pattern. The paper also concentrates on illustrating the algorithm for finding the cameras where the QoS needs to be harmonized. It shows how to make the algorithm opportunistic, so that the number of messages in the network can be reduced while tweaking the QoS.

Design of a Parallel Face Detection Algorithm for Distributed Low Cost IP-based Surveillance Systems

In an environment with high secure requirement, multiple video surveillance devices may be installed in the secure environment. In this paper, a multi-camera surveillance system is built for these environments by a set of low-cost IP-based video surveillance cameras. An embedded distributed computing library, called D2MCE, is used to support the distributed computing. It provides a set of easy-to-use interfaces for the programmers to develop distributed parallel programs in a logically shared memory space. The proposed algorithm consists of four phases, including simultaneous intruder detection (SID), distributed camera selection (DCS), distributed & parallel face detection algorithm (DP-FDA), and automatic CAMShift face tracker (ACAM-FT). The advantage is that the system response time for the face detection will be shortened on the low-cost embedded IP-cameras without the help of expensive server machines. The system has been implemented on a set of the Intel PXA270 embedded platforms. Experiment results show that good performance can be achieved on the system.