Critical Video Quality for Distributed Automated Video Surveillance

Large-scale distributed video surveillance systems pose new scalability challenges. Due to the large number of video sources in such systems, the amount of bandwidth required to transmit video streams for monitoring often strains the capability of the network. On the other hand, large-scale surveillance systems often rely on computer vision algorithms to automate surveillance tasks. We observe that these surveillance tasks present an opportunity for trade-off between the accuracy of the tasks and the bit rate of the video being sent. This paper shows that there exists a sweet spot, which we term critical video quality that can be used to reduce video bit rate without significantly affecting the accuracy of the surveillance tasks. We demonstrate this point by running extensive experiments on standard face detection and face tracking algorithms. Our experiments show that face detection works equally well even if the quality of compression is significantly reduced, and face tracking still works even if the frame rate is reduced to 6 frames per second. We further develop a prototype video surveillance system to demonstrate this idea. Our evaluation shows that we can achieve up to 29 times reduction in video bit rate when detecting faces and 16 times reduction when tracking faces. This paper also proposes a formal rate-accuracy optimization framework which can be used to determine appropriate encoding parameters in distributed video surveillance systems that are subjected to either bandwidth constraints or accuracy constraints

Autonomous monitoring of cliff nesting seabirds using computer vision

In this paper we describe a proposed system for automatic visual monitoring of seabird populations. Image sequences of cliff face nesting sites are captured using time-lapse digital photography. We are developing image processing software which is designed to automatically interpret these images, determine the number of birds present, and monitor activity. We focus primarily on the the development of low-level image processing techniques to support this goal. We first describe our existing work in video processing, and show how it is suitable for this problem domain. Image samples from a particular nest site are presented, and used to describe the associated challenges. We conclude by showing how we intend to develop our work to construct a distributed system capable of simultaneously monitoring a number of sites in the same locality

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

Towards a cloud‑based automated surveillance system using wireless technologies

Cloud Computing can bring multiple benefits for Smart Cities. It permits the easy creation of centralized knowledge bases, thus straightforwardly enabling that multiple embedded systems (such as sensor or control devices) can have a collaborative, shared intelligence. In addition to this, thanks to its vast computing power, complex tasks can be done over low-spec devices just by offloading computation to the cloud, with the additional advantage of saving energy. In this work, cloud’s capabilities are exploited to implement and test a cloud-based surveillance system. Using a shared, 3D symbolic world model, different devices have a complete knowledge of all the elements, people and intruders in a certain open area or inside a building. The implementation of a volumetric, 3D, object-oriented, cloud-based world model (including semantic information) is novel as far as we know. Very simple devices (orange Pi) can send RGBD streams (using kinect cameras) to the cloud, where all the processing is distributed and done thanks to its inherent scalability. A proof-of-concept experiment is done in this paper in a testing lab with multiple cameras connected to the cloud with 802.11ac wireless technology. Our results show that this kind of surveillance system is possible currently, and that trends indicate that it can be improved at a short term to produce high performance vigilance system using low-speed devices. In addition, this proof-of-concept claims that many interesting opportunities and challenges arise, for example, when mobile watch robots and fixed cameras would act as a team for carrying out complex collaborative surveillance strategies.Ministerio de Economía y Competitividad TEC2016-77785-PJunta de Andalucía P12-TIC-130

Can we ID from CCTV? Image quality in digital CCTV and face identification performance

CCTV is used for an increasing number Of purposes, and the new generation of digital systems can be tailored to serve a wide range of security requirements. However, configuration decisions are often made without considering specific task requirements, e.g. the video quality needed for reliable person identification. Our Study investigated the relationship between video quality and the ability of untrained viewers to identify faces from digital CCTV images. The task required 80 participants to identify 64 faces belonging to 4 different ethnicities. Participants compared face images taken from a high quality photographs and low quality CCTV stills, which were recorded at 4 different video quality bit rates (32, 52, 72 and 92 Kbps). We found that the number of correct identifications decreased by 12 (similar to 18%) as MPEG-4 quality decreased from 92 to 32 Kbps, and by 4 (similar to 6%) as Wavelet video quality decreased from 92 to 32 Kbps. To achieve reliable and effective face identification, we recommend that MPEG-4 CCTV systems should be used over Wavelet, and video quality should not be lowered below 52 Kbps during video compression. We discuss the practical implications of these results for security, and contribute a contextual methodology for assessing CCTV video quality

Regional Data Archiving and Management for Northeast Illinois

This project studies the feasibility and implementation options for establishing a regional data archiving system to help monitor and manage traffic operations and planning for the northeastern Illinois region. It aims to provide a clear guidance to the regional transportation agencies, from both technical and business perspectives, about building such a comprehensive transportation information system. Several implementation alternatives are identified and analyzed. This research is carried out in three phases. In the first phase, existing documents related to ITS deployments in the broader Chicago area are summarized, and a thorough review is conducted of similar systems across the country. Various stakeholders are interviewed to collect information on all data elements that they store, including the format, system, and granularity. Their perception of a data archive system, such as potential benefits and costs, is also surveyed. In the second phase, a conceptual design of the database is developed. This conceptual design includes system architecture, functional modules, user interfaces, and examples of usage. In the last phase, the possible business models for the archive system to sustain itself are reviewed. We estimate initial capital and recurring operational/maintenance costs for the system based on realistic information on the hardware, software, labor, and resource requirements. We also identify possible revenue opportunities. A few implementation options for the archive system are summarized in this report; namely: 1. System hosted by a partnering agency 2. System contracted to a university 3. System contracted to a national laboratory 4. System outsourced to a service provider The costs, advantages and disadvantages for each of these recommended options are also provided.ICT-R27-22published or submitted for publicationis peer reviewe