An informatics-based approach to object tracking for distributed live video computing

Omnipresent camera networks have been a popular research topic in recent years. They are applicable to a range of monitoring tasks, from bridges to gas stations to the inside of industrial chemical tanks. Though a large body of existing work focuses on image and video processing techniques, very few address the usability of such systems or the implications of real-time video dissemination. In this article, we present our work on extending the LVDBMS prototype with a multifaceted object model to characterize objects in live video streams. This forms the basis for a cross-camera tracking framework which permits objects to be tracked from one video stream to another. With this infrastructure, real-time queries may be posed to monitor complex events that occur in multiple video streams simultaneously. This live video database environment provides a general-purpose platform for distributed live video computing with the goal of enabling rapid application development for camera networks.     

Privacy Ontology Support for E-Commerce

Privacy is becoming increasingly important due to the advent of e-commerce. E-commerce applications frequently require customers to divulge many personal details about themselves that must be protected carefully in accordance with privacy principles and regulations. Here, the authors define a privacy ontology to support the provision of privacy and help derive the level of privacy associated with e-commerce transactions and applications. The privacy ontology provides a framework against which e-commerce sites can benchmark their privacy policies and implementations.     

Understanding trust in privacy-aware video surveillance systems

Recent advances in pervasive video surveillance systems pave the way for a comprehensive surveillance of every aspect of our lives, hence, leading us to a state of dataveillance. Computerized and interconnected systems of cameras could be used to profile, track and monitor individuals for the sake of security. Notwithstanding, these systems clearly interfere with the fundamental right of the individuals to privacy. Most literature on privacy in video surveillance systems concentrates on the goal of detecting faces and other regions of interest and in proposing different methods to protect them. However, the trustworthiness of those systems and, by extension, of the privacy they provide are mostly neglected. In this article, we define the concept of trustworthy privacy-aware video surveillance system. Moreover, we assess the techniques proposed in the literature according to their suitability for such a video surveillance system. Finally, we describe the properties that a deployment of a trustworthy video surveillance system must fulfill.     

基于视觉的交通监控系统固定背景区段视频传输的研究

多年来,视频图像传输总是依赖于图像压缩比和网络带宽之间的权衡来实现。根据具有固定背景的基于视觉的交通监控系统的视频传输特点采用单参考图背景差提取对象的方法研究,提出了一种基于特定对象的图像传输方式:(1)固定背景分别在采集端与控制中心预存;(2)采集端应用单参照图背景差方法提取目标对象,生成最小矩形覆盖网格区域及其掩码矩阵;(3)网络通道可以根据应用情况灵活选择;(4)控制中心则根据目标对象编码或其小波变换结果以及网格区域和掩码在背景图上重构出指定背景中目标运动视频。其优势在于:(1)大量节省网络带宽;(2)提供系统高级处理的有效输入如空间位置关系图以及可以校正的目标对象速度数据;(3)便于操作人员注意主要预警事件;(4)为系统扩能提供空间等。实验结果表明了这种方法的可行性。     

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.     

Towards high performance security policy evaluation

The Enterprise Privacy Authorization Language (EPAL) is a formal language for specifying fine-grained enterprise privacy policies. With the adoption of EPAL, especially in web applications, the performance of EPAL policy evaluation engines becomes a critical issue. In this paper, we propose Eengine, an engine for efficient EPAL policy evaluation. Eengine first converts all string values in an EPAL policy to numerical values. Second, it converts a numericalized EPAL policy specified as a list of rules following the first-match semantics to a tree structure for efficient processing of numericalized requests.     

Hierarchical database for a multi-camera surveillance system

This paper presents a framework for event detection and video content analysis for visual surveillance applications. The system is able to coordinate the tracking of objects between multiple camera views, which may be overlapping or non-overlapping. The key novelty of our approach is that we can automatically learn a semantic scene model for a surveillance region, and have defined data models to support the storage of tracking data with different layers of abstraction into a surveillance database. The surveillance database provides a mechanism to generate video content summaries of objects detected by the system across the entire surveillance region in terms of the semantic scene model. In addition, the surveillance database supports spatio-temporal queries, which can be applied for event detection and notification applications.     

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.     

Privacy issues in a psychiatric context: applying the ISD privacy framework to a psychiatric behavioural monitoring system

Privacy issues are frequently discussed amongst researchers, practitioners and patients in healthcare. However, psychiatric patients’ privacy issues get less attention in information system development (ISD), whereby they are one of the most important stakeholders. This paper applies Carew and Stapleton’s ISD privacy framework to psychiatric monitoring systems to understand the issues that are related to monitoring psychiatric patients’ behaviour. By understanding the privacy issues amongst patients, the research will be able to provide guidance to system developers to produce a privacy-sensitive system that can contribute to the system engineering for international stability. We will elaborate each factor in the framework (physical, social, psychological and informational) and then explain the relationship to the privacy of psychiatric patient.     

Real‐time smart surveillance using motion analysis

Abstract: In the last years, smart surveillance has been one of the most active research topics in computer vision because of the wide spectrum of promising applications. Its main point is about the use of automatic video analysis technologies for surveillance purposes. In general, a processing framework for smart surveillance consists of a preliminary motion detection step in combination with high‐level reasoning that allows automatic understanding of evolutions of observed scenes. In this paper, we propose a surveillance framework based on a set of reliable visual algorithms that perform different tasks: a motion analysis approach that segments foreground regions is followed by three procedures, which perform object tracking, homographic transformations and edge matching, in order to achieve the real‐time monitoring of forbidden areas and the detection of abandoned or removed objects. Several experiments have been performed on different real image sequences acquired from a Messapic museum (indoor context) and the nearby archaeological site (outdoor context) to demonstrate the effectiveness and the flexibility of the proposed approach.     

Real-time background generation and foreground object segmentation for high-definition colour video stream in FPGA device

The processing of a high-definition video stream in real-time is a challenging task for embedded systems. However, modern FPGA devices have both a high operating frequency and sufficient logic resources to be successfully used in these tasks. In this article, an advanced system that is able to generate and maintain a complex background model for a scene as well as segment the foreground for an HD colour video stream (1,920 × 1,080 @ 60 fps) in real-time is presented. The possible application ranges from video surveillance to machine vision systems. That is, in all cases, when information is needed about which objects are new or moving in the scene. Excellent results are obtained by using the CIE Lab colour space, advanced background representation as well as integrating information about lightness, colour and texture in the segmentation step. Finally, the complete system is implemented in a single high-end FPGA device.     

基于Spark Streaming的视频/图像流处理与新的性能评估方法

智能视频监控技术在公共安全、交通管理、智慧城市等方面有着广泛的运用前景,需求日益增长。随着摄像头安装的数量越来越多,采集的图像数据量越来越大,靠单台计算机处理已经远远不能满足需求了。分布式计算的兴起与发展为解决大规模的数据处理问题提供了很好的途径。使用一种基于Spark Streaming的视频/图像流处理的测试平台,阐述了平台的构成和工作流程,深入研究各个参数对集群性能的影响,创新性地提出了CPU时间占用率作为性能评估指标,与总的处理时间结合,更为全面反映集群性能和资源利用率。     

Fuzzy diffusion filter with extended neighborhood

Anisotropic diffusion filters, which are motivated from heat diffusion between mediums, have become a widely used technique in the field of image processing. In the initial proposals of anisotropic diffusion filters, 4-neighborhood values with diffusivity functions are computed independently for each spatial location because of numerical approximation. However, anisotropic diffusion filters could not be used in real-time image and video processing applications because they need diffusivity parameters, which must be specified by users in every sampling period. In this study, a fuzzy adaptive diffusion filter using extended neighborhood without diffusivity functions has been developed. The fuzzy adaptive diffusion filter does not require any parameter chosen by user and therefore they could be employed in real-time applications. In the fuzzy adaptive diffusion filter, a similarity transformation by means of relation matrix and fuzzy logic is carried out. Accordingly, the similarity image, output of transformation, is directly used as a heat diffusion coefficient in the diffusion filter. Results show that the fuzzy adaptive diffusion filter is very efficient for removing noise in image while preserving edges.     

User-centric privacy awareness in video surveillance

Especially in urban environments, video cameras have become omnipresent. Supporters of video surveillance argue that it is an excellent tool for many applications including crime prevention and law enforcement. While this is certainly true, it must be questioned if sufficient efforts are made to protect the privacy of monitored people. Privacy concerns are often set aside when compared to public safety and security. One reaction to this situation is emerging: community-based efforts where citizens register and map surveillance cameras in their environment. Our study is inspired by this idea and proposes a user-specific and location-aware privacy awareness system. Using conventional smartphones, users not only can contribute to the camera maps, but also use community-collected data to be alerted of potential privacy violations. In our model, we define different levels of privacy awareness. For the highest level, we present a mechanism that allows users to directly interact with specially designed, trustworthy cameras. These cameras provide direct feedback about the tasks that are executed by the camera and how privacy-sensitive data is handled. A hardware security chip that is integrated into the camera is used to ensure authenticity, integrity and freshness of the provided camera status information.     

基于Hadoop的离线视频处理技术研究与实现

当前,智慧城市成为信息时代城市建设的一个基本目标,智能视频安防监控是其中重要一环,希望从视频图像提取出有效的信息,提供有效的治安防控业务信息。由于视频监控系统广泛使用于各行各业,监控视频数据已成为一类典型的大数据,因此,如何对监控视频大数据进行高效的处理成为一个重要挑战。为此,本文在分析视频处理特点的基础上,提出并实现了一种基于HadoopMapReduce计算框架的分布式离线视频处理方法,该方法根据视频处理的特点进行优化,提升了监控视频大数据的处理效率。     

Compliance to the fair information practices: How are the Fortune 500 handling online privacy disclosures?

Privacy concerns and practices, especially those dealing with the acquisition and use of consumer personal information, are at the forefront of global business and social issues associated with the information age. Our research examined the privacy policies of the Fortune 500 to assess the substance and content of their stated information practices and the degree to which they adhered to the fair information practices (FIP).From the observations, we developed a Privacy Policy Assessment Matrix that can be used to evaluate how well a firm addresses information privacy concerns. The matrix was used to analyze the Fortune 500 firms to understand their privacy maturity. The results provided practical and theoretical implications for addressing information privacy issues.     

基于腐蚀滤波的运动目标检测的图像处理

在智能化数字视频监控系统中,获得的运动目标检测结果,经常存在一定程度的噪声。如果不对其去除,就会影响后续的图像处理质量。论文在对传统均值滤波研究的基础上,提出了一种形态学处理中的腐蚀滤波方法,对图像进行去噪,来改善检测结果的质量,以提高运动目标检测的精确度和视觉效果。通过对实际运动目标检测结果进行去噪处理,图像中的噪声点基本被删除,达到了预期的效果,防止了误检测。实验结果表明,该方法具有实用性和有效性。     

Design and implementation of a secure and trustworthy platform for privacy-aware video surveillance

Worldwide, thousands of video surveillance cameras record our daily activities. People are aware that video surveillance is deployed for the sake of security. However, the privacy of individuals would be endangered if the proper measures were not considered. Privacy-aware video surveillance has historically been addressed by proposals based on detecting individuals and other sensitive parts of the video and hiding them using a variety of techniques. In this paper, we present a comprehensive solution tackling video processing, video protection and management of the Information System. We claim that a video surveillance system can protect our safety and, at the same time, guarantee our privacy. We describe the design and implementation of a privacy-aware video surveillance platform that, in order to be trustworthy, accomplishes with the properties of high detection accuracy, real-time performance and protected video utility. We have tested the proposed platform, and we demonstrate the feasibility of our approach for privacy protection.     

Distributed interactive video arrays for event capture and enhanced situational awareness

Video surveillance activity has dramatically increased over the past few years. Earlier work dealt mostly with single stationary cameras, but the recent trend is toward active multicamera systems. Such systems offer several advantages over single camera systems - multiple overlapping views for obtaining 3D information and handling occlusions, multiple nonoverlapping cameras for covering wide areas, and active pan-tilt-zoom (PTZ) cameras for observing object details. To address these issues, we have developed a multicamera video surveillance approach, called distributed interactive video array. The DIVA framework provides multiple levels of semantically meaningful information ("situational" awareness) to match the needs of multiple remote observers. We have designed DIVA-based systems that can track and identify vehicles and people, monitor perimeters and bridges, and analyze activities. A new video surveillance approach employing a large-scale cluster of video sensors demonstrates the promise of multicamera arrays for homeland security.