Performance modelling and optimization for video-analytic algorithms in a cloud-like environment using machine learning

CCTV cameras produce a large amount of video surveillance data per day, and analysing them require the use of significant computing resources that often need to be scalable. The emergence of the Hadoop distributed processing framework has had a significant impact on various data intensive applications as the distributed computed based processing enables an increase of the processing capability of applications it serves. Hadoop is an open source implementation of the MapReduce programming model. It automates the operation of creating tasks for each function, distribute data, parallelize executions and handles machine failures that reliefs users from the complexity of having to manage the underlying processing and only focus on building their application. It is noted that in a practical deployment the challenge of Hadoop based architecture is that it requires several scalable machines for effective processing, which in turn adds hardware investment cost to the infrastructure. Although using a cloud infrastructure offers scalable and elastic utilization of resources where users can scale up or scale down the number of Virtual Machines (VM) upon requirements, a user such as a CCTV system operator intending to use a public cloud would aspire to know what cloud resources (i.e. number of VMs) need to be deployed so that the processing can be done in the fastest (or within a known time constraint) and the most cost effective manner. Often such resources will also have to satisfy practical, procedural and legal requirements. The capability to model a distributed processing architecture where the resource requirements can be effectively and optimally predicted will thus be a useful tool, if available. In literature there is no clear and comprehensive modelling framework that provides proactive resource allocation mechanisms to satisfy a user's target requirements, especially for a processing intensive application such as video analytic. In this thesis, with the hope of closing the above research gap, novel research is first initiated by understanding the current legal practices and requirements of implementing video surveillance system within a distributed processing and data storage environment, since the legal validity of data gathered or processed within such a system is vital for a distributed system's applicability in such domains. Subsequently the thesis presents a comprehensive framework for the performance ii modelling and optimization of resource allocation in deploying a scalable distributed video analytic application in a Hadoop based framework, running on virtualized cluster of machines. The proposed modelling framework investigates the use of several machine learning algorithms such as, decision trees (M5P, RepTree), Linear Regression, Multi Layer Perceptron(MLP) and the Ensemble Classifier Bagging model, to model and predict the execution time of video analytic jobs, based on infrastructure level as well as job level parameters. Further in order to propose a novel framework for the allocate resources under constraints to obtain optimal performance in terms of job execution time, we propose a Genetic Algorithms (GAs) based optimization technique. Experimental results are provided to demonstrate the proposed framework's capability to successfully predict the job execution time of a given video analytic task based on infrastructure and input data related parameters and its ability determine the minimum job execution time, given constraints of these parameters. Given the above, the thesis contributes to the state-of-art in distributed video analytics, design, implementation, performance analysis and optimisation

Security-Camera Proposal for the Dynamy Youth Center

Dynamy is an educational program that sponsors a Youth Center for high schools students. Recently, Dynamy officials have considered security cameras to secure the building from outsiders. To assess Dynamy\u27s Security Camera needs, I went to the University of Pennsylvania to learn from the most secure educational facility in America. I also met with professional CCTV installers from ADT Security, who even gave a free on-site estimate. I was able to draft a security camera proposal for the Dynamy Youth Center. The proposal asks for 8 cameras to be installed by Dynamy officials to secure the facility\u27s computer labs, conference rooms, office areas, and entrance ways. The security camera proposal explains how to buy a CCTV system, where to place cameras, and how to route cabling

“Bang!”: ShotSpotter Gunshot Detection Technology, Predictive Policing, and Measuring Terry’s Reach

ShotSpotter technology is a rapid identification and response system used in ninety American cities that is designed to detect gunshots and dispatch police. ShotSpotter is one of many powerful surveillance tools used by local police departments to purportedly help fight crime, but they often do so at the expense of infringing upon privacy rights and civil liberties. This Article expands the conversation about ShotSpotter technology considerably by examining the adjacent Fourth Amendment issues emanating from its use. For example, law enforcement increasingly relies on ShotSpotter to create reasonable suspicion where it does not exist. In practice, the use of ShotSpotter increases the frequency of police interactions, which also increases the risk of Black Americans becoming the victims of police brutality or harassment. Such racialized policing facilitates the status quo of violence and bias against Black Americans. This Article uses recent cases from the D.C., the Fourth, and Seventh Circuits as a foundation to argue that officers arriving on the scene to investigate a gunshot sound they were alerted of via ShotSpotter technology should not be allowed to use the gunshot sound as the basis of reasonable suspicion and subsequent search and seizure. At the heart of this Article is the argument that the use of ShotSpotter technology is unconstitutional under City of Indianapolis v. Edmond because it is not used for a specific law enforcement purpose beyond preventing crime generally. Under the reasoning and result of Edmond, law enforcement is prohibited from using ShotSpotters unless officers have reasons for individualized suspicion. Spending more money on ineffective ShotSpotters placed in “high crime” neighborhoods across America is not the answer to reducing gun violence. As seen with Oakland’s successful Ceasefire program, there are innovative ways to simultaneously build trust in communities and curb gun violence. Indeed, properly designed group violence reduction strategies will foster and maintain dignity for participants in a program tailored to saves lives and promote community healing

The Proceedings of 14th Australian Digital Forensics Conference, 5-6 December 2016, Edith Cowan University, Perth, Australia

Conference Foreword This is the fifth year that the Australian Digital Forensics Conference has been held under the banner of the Security Research Institute, which is in part due to the success of the security conference program at ECU. As with previous years, the conference continues to see a quality papers with a number from local and international authors. 11 papers were submitted and following a double blind peer review process, 8 were accepted for final presentation and publication. Conferences such as these are simply not possible without willing volunteers who follow through with the commitment they have initially made, and I would like to take this opportunity to thank the conference committee for their tireless efforts in this regard. These efforts have included but not been limited to the reviewing and editing of the conference papers, and helping with the planning, organisation and execution of the conference. Particular thanks go to those international reviewers who took the time to review papers for the conference, irrespective of the fact that they are unable to attend this year. To our sponsors and supporters a vote of thanks for both the financial and moral support provided to the conference. Finally, to the student volunteers and staff of the ECU Security Research Institute, your efforts as always are appreciated and invaluable. Yours sincerely, Conference Chair Professor Craig Valli Director, Security Research Institut

Real-time transmission and storage of video, audio, and health data in emergency and home care situations

The increase in the availability of bandwidth for wireless links, network integration, and the computational power on fixed and mobile platforms at affordable costs allows nowadays for the handling of audio and video data, their quality making them suitable for medical application. These information streams can support both continuous monitoring and emergency situations. According to this scenario, the authors have developed and implemented the mobile communication system which is described in this paper. The system is based on ITU-T H.323 multimedia terminal recommendation, suitable for real-time data/video/audio and telemedical applications. The audio and video codecs, respectively, H.264 and G723.1, were implemented and optimized in order to obtain high performance on the system target processors. Offline media streaming storage and retrieval functionalities were supported by integrating a relational database in the hospital central system. The system is based on low-cost consumer technologies such as general packet radio service (GPRS) and wireless local area network (WLAN or WiFi) for lowband data/video transmission. Implementation and testing were carried out for medical emergency and telemedicine application. In this paper, the emergency case study is described

CoupleNet: Coupling Global Structure with Local Parts for Object Detection

The region-based Convolutional Neural Network (CNN) detectors such as Faster R-CNN or R-FCN have already shown promising results for object detection by combining the region proposal subnetwork and the classification subnetwork together. Although R-FCN has achieved higher detection speed while keeping the detection performance, the global structure information is ignored by the position-sensitive score maps. To fully explore the local and global properties, in this paper, we propose a novel fully convolutional network, named as CoupleNet, to couple the global structure with local parts for object detection. Specifically, the object proposals obtained by the Region Proposal Network (RPN) are fed into the the coupling module which consists of two branches. One branch adopts the position-sensitive RoI (PSRoI) pooling to capture the local part information of the object, while the other employs the RoI pooling to encode the global and context information. Next, we design different coupling strategies and normalization ways to make full use of the complementary advantages between the global and local branches. Extensive experiments demonstrate the effectiveness of our approach. We achieve state-of-the-art results on all three challenging datasets, i.e. a mAP of 82.7% on VOC07, 80.4% on VOC12, and 34.4% on COCO. Codes will be made publicly available.Comment: Accepted by ICCV 201