Semantic web technologies for video surveillance metadata

Video surveillance systems are growing in size and complexity. Such systems typically consist of integrated modules of different vendors to cope with the increasing demands on network and storage capacity, intelligent video analytics, picture quality, and enhanced visual interfaces. Within a surveillance system, relevant information (like technical details on the video sequences, or analysis results of the monitored environment) is described using metadata standards. However, different modules typically use different standards, resulting in metadata interoperability problems. In this paper, we introduce the application of Semantic Web Technologies to overcome such problems. We present a semantic, layered metadata model and integrate it within a video surveillance system. Besides dealing with the metadata interoperability problem, the advantages of using Semantic Web Technologies and the inherent rule support are shown. A practical use case scenario is presented to illustrate the benefits of our novel approach.     

Internet of surveillance: a cloud supported large-scale wireless surveillance system

Large-scale video surveillance systems are among the necessities for securing our life these days. The high bandwidth demand and the large storage requirements are the main challenges in such systems. To face these challenges, the system can be deployed as a multi-tier framework that utilizes different technologies. In such a framework, technologies proposed under the umbrella of the Internet of Things (IoT) can play a significant rule in facing the challenges. In video surveillance, the cameras can be considered as “the things” that are streaming videos to a central processing and storage server (the cloud) through the Internet. Wireless technologies can be used to connect wireless cameras to the surveillance system more conveniently than wired cameras. Unfortunately, wireless communication in general tend to have limited bandwidth that needs careful management to achieve scalability. In this paper, we design and evaluate a reliable IoT-based wireless video surveillance system that provides an optimal bandwidth distribution and allocation to minimize the overall surveillance video distortion. We evaluate our system using NS-3 simulation. The results show that the proposed framework fully utilizes the available cloud bandwidth budget and achieves high scalability.     

多媒体技术研究:2013——面向智能视频监控的视觉感知与处理

目的 随着视频监控技术的日益成熟和监控设备的普及,视频监控应用日益广泛,监控视频数据量呈现出爆炸性的增长,已经成为大数据时代的重要数据对象。然而由于视频数据本身的非结构化特性,使得监控视频数据的处理和分析相对困难。面对大量摄像头采集的监控视频大数据,如何有效地按照视频的内容和特性去传输、存储、分析和识别这些数据,已经成为一种迫切的需求。方法 本文面向智能视频监控中大规模视觉感知与智能处理问题,围绕监控视频编码、目标检测与跟踪、监控视频增强、视频运动与异常行为识别等4个主要研究方向,系统阐述2013年度的技术发展状况,并对未来的发展趋势进行展望。结果 中国最新制定的国家标准AVS2在对监控视频的编码效率上比最新国际标准H.265/HEVC高出一倍,标志着我国的视频编码技术和标准在视频监控领域已经实现跨越;视频运动目标检测跟踪的研究主要集中在有效特征提取和分类器训练等方面,机器学习等方法的引入,使得基于多实例学习、稀疏表示的运动目标检测跟踪成为研究的热点;监控视频质量增强主要包括去雾、去夜色、去雨雪、去模糊和超分辨率增强等多方面的内容,现有的算法均是对某类图像清晰化效果较好,而对其他类则相对较差,普适性不高;现有的智能动作分析与异常行为识别技术虽然得到了不断发展,算法的性能也在不断提高,但是从实用角度,除了简单的特定或可控场景外,还没有太多成熟的应用系统。结论 随着大数据时代的到来,智能视频监控的需求将日益迫切,面对众多挑战的同时,该研究领域将迎来前所未有的重大机遇,必将产生越来越多可以实用的研究成果。     

改进的基于高斯混合模型的运动目标检测算法

针对固定场景视频监控中,由于运动物体在运动目标检测算法初始化时的存在而导致传统的基于高斯混合模型的运动目标检测算法收敛速度慢的问题,提出了改进算法。该改进算法通过采用在线K-均值聚类方法对混合高斯模型进行初始化,提高了算法的收敛速度。同时在模型更新时,通过对匹配准则和新高斯分布生成准则的改进,节约了存储空间。实验结果表明,与传统算法相比,改进算法能够快速、有效地检测运动目标,具有更好的鲁棒性。     

OCULUS surveillance system: Fuzzy on-line speed analysis from 2D images

This paper presents an independent component integrated into a global surveillance system named as OCULUS. The aim of this component is to classify the speed of moving objects as normal or abnormal in order to detect anomalous events, taking into account the object class and spatio-temporal information such as locations and movements. The proposed component analyses the speed of the detected objects in real-time without needing several cameras, a 3D representation of the environment, or the estimation of precise values. Unlike other works, the proposed method does require knowing the camera parameters previously (e.g. height, angle, zoom level, etc.). The knowledge used by this component is automatically acquired by means of a learning algorithm that generates a set of highly interpretable fuzzy rules. The experimental results demonstrate that the proposed method is accurate, robust and provides a real-time analysis.     

Automated camera sabotage detection for enhancing video surveillance systems

Surveillance cameras are vital source of information in crime investigations. A surveillance video must be recorded with correct field of view and be of good quality, otherwise, it may not be suitable for investigation or analysis purposes. Perpetrators may tamper the recorded video or the physical device itself, in order to conceal their dubious activities. Generally, surveillance systems are unmanned due to limitations of manual monitoring. Automatic detection of camera tamper events is crucial for timely operator intervention. We propose a new method for detecting video camera tampering events like occlusion, defocus and displacement. The features used are edge information, frame count, foreground objects’ coverage area and its static nature. Effectiveness of our method is tested through experimentation on public datasets. The results obtained are encouraging with high detection and low false alarm rates. The proposed method automatically detects routine problems with cameras like dirt on camera lens, fog and smoke.

     

Semi-supervised vision-based maritime surveillance system using fused visual attention maps

This paper presents a vision-based system for maritime surveillance, using moving PTZ cameras. The proposed methodology fuses a visual attention method that exploits low-level image features appropriately selected for maritime environment, with appropriate tracker, without making any assumptions about environmental or visual conditions. The offline initialization is based on large graph semi-supervised technique. System’s performance was evaluated with videos from cameras placed at Limassol port and Venetian port of Chania. Results suggest high detection ability, despite dynamically changing visual conditions and different kinds of vessels, all in real time.     

基于上下文感知进程网络的Web服务组合方法

针对Web服务组合的个性化问题,提出一种基于上下文感知进程网络的Web服务组合方法,支持上下文感知组合系统的高层建模与系统的底层实现。采用CCS进程代数和标签转换系统,描述上下文感知组合模型的形式化语义,给出上下文感知系统模型的实现框架。分析结果证明了该组合方法的可行性。     

Sound based alarming based video surveillance system design

Multimedia Tools and Applications - Modern video surveillance systems consist of a network of many video cameras. Constantly video camera systems are being installed for security reasons in...     

Automated and coupled services of advanced smart surveillance systems toward green IT: tracking,retrieval and digital evidence

Green Security is a new research field defining and investigating security solutions using an energy-aware perspective. Growing efforts and interests for an intelligent or smart surveillance system which is capable of automatically detecting and tracking target objects is in the spotlight in the security community. So far, these technologies are mainly aimed at single camera applications and are evolving with the demand for wide-area surveillance systems currently. However, the tracking techniques used on a single camera have limitations in providing effective crime prevention and countermeasures when an incident occurs since an object is not linked to other cameras. In addition, the use of multi-camera systems for wide-area surveillance not only produces large amounts of video data to be stored, but also have more technical requirements in the interrelation between cameras or server. It require a considerable amount of time, manpower and energy in multi-camera tracking and back-tracking of objects. Therefore, we propose the advanced smart surveillance system for wide-areas which is capable of the automated tracking and retrieval of target object and digital evidence-video collection. Furthermore, we considered the multiple-camera environment with non-overlapping views which includes more constraint conditions by various light changes. This system enables real-time object tracking, fast post-retrieval and selective digital evidence collection with economy of time, manpower, memory devices, and energy consumption. Also, this system is more energy-efficient since our schemes are organically connected to each other.     

基于智能分析技术的高清视频监控系统的设计与实现

视频监控系统是安防中较为核心的软件产品,在安全系统中具有非常重要的地位。但是,在传统的视频监控系统中,视频信号的局限性很难满足现代用户对视频的需求。随着控制系统水平的提升,尤其是芯片处理技术以及视频分析技术的进步,推动了高清智能视频监控系统的发展。同时,高清智能视频监控系统具有网络和智能化分析,视频数据高清晰度等优势,更好地满足了高水平的智能视频控制﹐更好地保护了人们的人身和财产安全。     

An advanced video-based system for monitoring active volcanoes

Video surveillance systems are consolidated techniques for monitoring eruptive phenomena in volcanic areas. Along with these systems, which use standard video cameras, people working in this field sometimes make use of infrared cameras providing useful information about the thermal evolution of eruptions. Real-time analysis of the acquired frames is required, along with image storing, to analyze and classify the activity of volcanoes. Human effort and large storing capabilities are hence required to perform monitoring tasks.In this paper we present a new strategy aimed at improving the performance of video surveillance systems in terms of human-independent image processing and storing optimization. The proposed methodology is based on real-time thermo-graphic analysis of the area considered. The analysis is performed by processing images acquired with an IR camera and extracting information about meaningful volcanic events.Two software tools were developed. The first provides information about the activity being monitored and automatically adapts the image storing rate. The second tool automatically produces useful information about the eruptive activity encompassed by a selected frame sequence.The software developed includes a suitable user interface allowing for convenient management of the acquired images and easy access to information about the volcanic activity monitored.     

Constructing task visibility intervals for video surveillance

Vision systems are increasingly being deployed to perform complex surveillance tasks. While improved algorithms are being developed to perform these tasks, it is also important that data suitable for these algorithms be acquired – a non-trivial task in a dynamic and crowded scene viewed by multiple PTZ cameras. In this paper, we describe a real-time multi-camera system that collects images and videos of moving objects in such scenes, subject to task constraints. The system constructs “task visibility intervals” that contain information about what can be sensed in future time intervals. Constructing these intervals requires prediction of future object motion and consideration of several factors such as object occlusion and camera control parameters. Such intervals can also be combined to form multi-task intervals, during which a single camera can collect videos suitable for multiple tasks simultaneously. Experimental results are provided to illustrate the system capabilities in constructing such task visibility intervals, followed by scheduling them using a greedy algorithm.     

Intended human object detection for automatically protecting privacy in mobile video surveillance

With the recent popularization of mobile video cameras including camera phones, a new technology, mobile video surveillance, which uses mobile video cameras for video surveillance has been emerging. Such videos, however, may infringe upon the privacy of others by disclosing privacy sensitive information (PSI), i.e., their appearances. To prevent videos from infringing on the right to privacy, new techniques are required that automatically obscure PSI regions. The problem is how to determine the PSI regions to be obscured while maintaining enough video content to present the camera persons’ capture-intentions, i.e., what they want to record in their videos to achieve their surveillance tasks. To this end, we introduce a new concept called intended human objects that are defined as human objects essential for capture-intentions, and develop a new method called intended human object detection that automatically detects the intended human objects in videos taken by different camera persons. Through the process of intended human object detection, we develop a system for automatically obscuring PSI regions. We experimentally show the performance of intended human object detection and the contributions of the features used. Our user study shows the potential applicability of our proposed system.     

Risk assessment for a video surveillance system based on Fuzzy Cognitive Maps

For various IT systems security is considered a key quality factor. In particular, it might be crucial for video surveillance systems, as their goal is to provide continuous protection of critical infrastructure and other facilities. Risk assessment is an important activity in security management; it aims at identifying assets, threats and vulnerabilities, analysis of implemented countermeasures and their effectiveness in mitigating risks. This paper discusses an application of a new risk assessment method, in which risk calculation is based on Fuzzy Cognitive Maps (FCMs) to a complex automated video surveillance system. FCMs are used to capture dependencies between assets and FCM based reasoning is applied to aggregate risks assigned to lower-level assets (e.g. cameras, hardware, software modules, communications, people) to such high level assets as services, maintained data and processes. Lessons learned indicate, that the proposed method is an efficient and low-cost approach, giving instantaneous feedback and enabling reasoning on effectiveness of security system.     

Anonymous subject identification and privacy information management in video surveillance

The widespread deployment of surveillance cameras has raised serious privacy concerns, and many privacy-enhancing schemes have been recently proposed to automatically redact images of selected individuals in the surveillance video for protection. Of equal importance are the privacy and efficiency of techniques to first, identify those individuals for privacy protection and second, provide access to original surveillance video contents for security analysis. In this paper, we propose an anonymous subject identification and privacy data management system to be used in privacy-aware video surveillance. The anonymous subject identification system uses iris patterns to identify individuals for privacy protection. Anonymity of the iris-matching process is guaranteed through the use of a garbled-circuit (GC)-based iris matching protocol. A novel GC complexity reduction scheme is proposed by simplifying the iris masking process in the protocol. A user-centric privacy information management system is also proposed that allows subjects to anonymously access their privacy information via their iris patterns. The system is composed of two encrypted-domain protocols: The privacy information encryption protocol encrypts the original video records using the iris pattern acquired during the subject identification phase; the privacy information retrieval protocol allows the video records to be anonymously retrieved through a GC-based iris pattern matching process. Experimental results on a public iris biometric database demonstrate the validity of our framework.     

Goal-based trajectory analysis for unusual behaviour detection in intelligent surveillance

In a typical surveillance installation, a human operator has to constantly monitor a large array of video feeds for suspicious behaviour. As the number of cameras increases, information overload makes manual surveillance increasingly difficult, adding to other confounding factors such as human fatigue and boredom. The objective of an intelligent vision-based surveillance system is to automate the monitoring and event detection components of surveillance, alerting the operator only when unusual behaviour or other events of interest are detected. While most traditional methods for trajectory-based unusual behaviour detection rely on low-level trajectory features such as flow vectors or control points, this paper builds upon a recently introduced approach that makes use of higher-level features of intentionality. Individuals in the scene are modelled as intentional agents, and unusual behaviour is detected by evaluating the explicability of the agent's trajectory with respect to known spatial goals. The proposed method extends the original goal-based approach in three ways: first, the spatial scene structure is learned in a training phase; second, a region transition model is learned to describe normal movement patterns between spatial regions; and third, classification of trajectories in progress is performed in a probabilistic framework using particle filtering. Experimental validation on three published third-party datasets demonstrates the validity of the proposed approach.     

A target-centric surveillance system based on localization and social networking

Surveillance systems are developed to enhance security and safety by constantly observing locations of interest. Although those systems can observe scenes of individual cameras separately, it is difficult to figure out what happened to the target moving across multiple cameras. This paper first proposes Video Diary Service (VDS) to solve this problem. VDS is an automatic video-oriented diary service, which keeps track of users’ lives. In addition, VDS can identify social networking relationships among the users, as well as record videos of the users. By exploiting these properties of VDS, we extend VDS into a new surveillance system called S-VDS. S-VDS is a target-centric surveillance system which focuses on the target, not the area, with its comprehensive information including the location, time, social relationship, and preferences. We then develop the basic functions of the proposed system and demonstrate its feasibility. We also illustrate three applications (i.e., a remote healthcare system, an anti-crime system, and a system for finding missing children), where the proposed system can enhance security and safety by considering individual surveillance purposes.     

Fusion of thermal infrared and visible spectra for robust moving object detection

The detection of moving objects is a crucial step for many video surveillance applications whether using a visible camera (VIS) or an infrared (IR) one. In order to profit from both types, several fusion methods were proposed in the literature: low-level fusion, medium-level fusion and high-level fusion. The first one is the most used for moving objects’ detection in IR and VIS spectra. In this paper, we present an overview of the different moving object detection methods in IR and VIS spectra and a state of the art of the low-level fusion techniques. Moreover, we propose a new method for moving object detection using low-level fusion of IR and VIS spectra. In order to evaluate quantitatively and qualitatively our proposed method, three series of experiments were carried out using two well-known datasets namely “OSU Color-Thermal Database” and “INO-Database”; the results of these evaluations show promising results and demonstrate the effectiveness of the proposed method.     

一种非重叠多摄像头的实时监控系统

大范围场景的监控需要使用多个摄像头。论文利用运动目标的颜色信息和路径特征,设计了一种非重叠多摄像头的实时监控系统。系统采用分布式多层次结构,在进行单摄像头层的处理时,根据像素点亮度变化检测和跟踪运动目标,同时获取运动目标的外形信息和路径特征;在进行多摄像头层的处理时,使用估计目标外形变化和建立路径模型方法融合多个摄像头信息,实现目标在非重叠多摄像头的跟踪。该系统不要求校准摄像头,也不要求建立完整的场景模型,即便在有亮度变化的环境中,仍能立即准确跟踪目标。实验证明提出的方法有好的跟踪效果。