Introduction to intelligent video surveillance system

Most surveillance systems today provide only a passive form of site monitoring. Extensive video records may be kept to help find the instigator of criminal activities after the crime has been committed but preventive measures usually require human involvement. In addition to this, there is a need for large amounts of data storage to keep up to several terabytes of video streams that may be needed for later analysis. For any sense of real-time monitoring, guards often need to be employed to watch video feeds for hours on end to recognize suspicious, dangerous or potentially harmful situations. In multi-camera scene monitoring systems, this becomes quite infeasible as there can be up to 20 to 50 cameras on average in a large complex such as an airport or Megamall. However, monitoring and storage space are not the only concerns. Even if these costs can be borne, there is the additional problem of reviewing this vast amount of video data after a crime or incident has occurre

Hierarchical video surveillance architecture: a chassis for video big data analytics and exploration

There is increasing reliance on video surveillance systems for systematic derivation, analysis and interpretation of the data needed for predicting, planning, evaluating and implementing public safety. This is evident from the massive number of surveillance cameras deployed across public locations. For example, in July 2013, the British Security Industry Association (BSIA) reported that over 4 million CCTV cameras had been installed in Britain alone. The BSIA also reveal that only 1.5% of these are state owned. In this paper, we propose a framework that allows access to data from privately owned cameras, with the aim of increasing the efficiency and accuracy of public safety planning, security activities, and decision support systems that are based on video integrated surveillance systems. The accuracy of results obtained from government-owned public safety infrastructure would improve greatly if privately owned surveillance systems ‘expose’ relevant video-generated metadata events, such as triggered alerts and also permit query of a metadata repository. Subsequently, a police officer, for example, with an appropriate level of system permission can query unified video systems across a large geographical area such as a city or a country to predict the location of an interesting entity, such as a pedestrian or a vehicle. This becomes possible with our proposed novel hierarchical architecture, the Fused Video Surveillance Architecture (FVSA). At the high level, FVSA comprises of a hardware framework that is supported by a multi-layer abstraction software interface. It presents video surveillance systems as an adapted computational grid of intelligent services, which is integration-enabled to communicate with other compatible systems in the Internet of Things (IoT)

Autonomous real-time surveillance system with distributed IP cameras

An autonomous Internet Protocol (IP) camera based object tracking and behaviour identification system, capable of running in real-time on an embedded system with limited memory and processing power is presented in this paper. The main contribution of this work is the integration of processor intensive image processing algorithms on an embedded platform capable of running at real-time for monitoring the behaviour of pedestrians. The Algorithm Based Object Recognition and Tracking (ABORAT) system architecture presented here was developed on an Intel PXA270-based development board clocked at 520 MHz. The platform was connected to a commercial stationary IP-based camera in a remote monitoring station for intelligent image processing. The system is capable of detecting moving objects and their shadows in a complex environment with varying lighting intensity and moving foliage. Objects moving close to each other are also detected to extract their trajectories which are then fed into an unsupervised neural network for autonomous classification. The novel intelligent video system presented is also capable of performing simple analytic functions such as tracking and generating alerts when objects enter/leave regions or cross tripwires superimposed on live video by the operator

AN IP-BASED LIVE DATABASE APPROACH TO SURVEILLANCE APPLICATION DEVELOPMENT

With the proliferation of inexpensive cameras, video surveillance applications are becoming ubiquitous in many domains such as public safety and security, manufacturing, intelligent transportation systems, and healthcare. IP-based video surveillance technologies, in particular, are able to bring traditional video surveillance centers to virtually any computer at any location with an Internet connection. Today’s IP-based video surveillance systems, however, are designed for specific classes of applications. For instance, one cannot use a system designed for incident detection on highways to monitor patients in a healthcare facility. To support rapid development of video surveillance applications, we designed and implemented a new class of general purpose database management system, the live video database management system (LVDBMS). We view networked IP cameras as a special class of storage devices, and allow the user to formulate ad hoc queries expressed over live video feeds. These continuous queries are processed in real time using novel distributed computing techniques. With this environment, the users are able to develop various specific web-based video surveillance systems for a variety of applications. These systems can coexist in a unified LVDBMS framework to share the expensive deployment and operating costs of the camera networks. Our contribution is the introduction of a live database approach to video surveillance software development. In this paper, we describe our prototype and present the live video data model, the query language, and the query processing technique. 1

Engineering ambient visual sensors

Visual sensors are an indispensable prerequisite for those AmI environments that require a surveillance component. One practical issue concerns maximizing the operational longevity of such sensors as the operational lifetime of an AmI environment itself is dependent on that of its constituent components. In this paper, the intelligent agent paradigm is considered as a basis for managing a camera collective such that the conflicting demands of power usage optimization and system performance are reconciled

A High-confidence Cyber-Physical Alarm System: Design and Implementation

Most traditional alarm systems cannot address security threats in a satisfactory manner. To alleviate this problem, we developed a high-confidence cyber-physical alarm system (CPAS), a new kind of alarm systems. This system establishes the connection of the Internet (i.e. TCP/IP) through GPRS/CDMA/3G. It achieves mutual communication control among terminal equipments, human machine interfaces and users by using the existing mobile communication network. The CPAS will enable the transformation in alarm mode from traditional one-way alarm to two-way alarm. The system has been successfully applied in practice. The results show that the CPAS could avoid false alarms and satisfy residents' security needs.Comment: IEEE/ACM Internet of Things Symposium (IOTS), in conjunction with GreenCom 2010, IEEE, Hangzhou, China, December 18-20, 201