"Not the Usual Suspects": A Study of Factors Reducing the Effectiveness of CCTV

Previous research on the effectiveness of Closed Circuit Television (CCTV) has focused on critically assessing police and government claims that CCTV is effective in reducing crime. This paper presents a field study that investigates the relationship between CCTV system design and the performance of operator tasks. We carried out structured observations and interviews with 13 managers and 38 operators at 13 CCTV control rooms. A number of failures were identified, including the poor configuration of technology, poor quality video recordings, and a lack of system integration. Stakeholder communication was poor, and there were too many cameras and too few operators. These failures have been previously identified by researchers; however, no design improvements have been made to control rooms in the last decade. We identify a number of measures to improve operator performance, and contribute a set of recommendations for security managers and practitioners. Security Journal (2010) 23, 134-154. doi:10.1057/sj.2008.2; published online 6 October 200

Automatic refocus and feature extraction of single-look complex SAR signatures of vessels

In recent years, spaceborne synthetic aperture radar ( SAR) technology has been considered as a complement to cooperative vessel surveillance systems thanks to its imaging capabilities. In this paper, a processing chain is presented to explore the potential of using basic stripmap single-look complex ( SLC) SAR images of vessels for the automatic extraction of their dimensions and heading. Local autofocus is applied to the vessels' SAR signatures to compensate blurring artefacts in the azimuth direction, improving both their image quality and their estimated dimensions. For the heading, the orientation ambiguities of the vessels' SAR signatures are solved using the direction of their ground-range velocity from the analysis of their Doppler spectra. Preliminary results are provided using five images of vessels from SLC RADARSAT-2 stripmap images. These results have shown good agreement with their respective ground-truth data from Automatic Identification System ( AIS) records at the time of the acquisitions.Postprint (published version

A modular software architecture for UAVs

There have been several attempts to create scalable and hardware independent software architectures for Unmanned Aerial Vehicles (UAV). In this work, we propose an onboard architecture for UAVs where hardware abstraction, data storage and communication between modules are efficiently maintained. All processing and software development is done on the UAV while state and mission status of the UAV is monitored from a ground station. The architecture also allows rapid development of mission-specific third party applications on the vehicle with the help of the core module

MusA: Using Indoor Positioning and Navigation to Enhance Cultural Experiences in a museum

In recent years there has been a growing interest into the use of multimedia mobile guides in museum environments. Mobile devices have the capabilities to detect the user context and to provide pieces of information suitable to help visitors discovering and following the logical and emotional connections that develop during the visit. In this scenario, location based services (LBS) currently represent an asset, and the choice of the technology to determine users' position, combined with the definition of methods that can effectively convey information, become key issues in the design process. In this work, we present MusA (Museum Assistant), a general framework for the development of multimedia interactive guides for mobile devices. Its main feature is a vision-based indoor positioning system that allows the provision of several LBS, from way-finding to the contextualized communication of cultural contents, aimed at providing a meaningful exploration of exhibits according to visitors' personal interest and curiosity. Starting from the thorough description of the system architecture, the article presents the implementation of two mobile guides, developed to respectively address adults and children, and discusses the evaluation of the user experience and the visitors' appreciation of these application

F-formation Detection: Individuating Free-standing Conversational Groups in Images

Detection of groups of interacting people is a very interesting and useful task in many modern technologies, with application fields spanning from video-surveillance to social robotics. In this paper we first furnish a rigorous definition of group considering the background of the social sciences: this allows us to specify many kinds of group, so far neglected in the Computer Vision literature. On top of this taxonomy, we present a detailed state of the art on the group detection algorithms. Then, as a main contribution, we present a brand new method for the automatic detection of groups in still images, which is based on a graph-cuts framework for clustering individuals; in particular we are able to codify in a computational sense the sociological definition of F-formation, that is very useful to encode a group having only proxemic information: position and orientation of people. We call the proposed method Graph-Cuts for F-formation (GCFF). We show how GCFF definitely outperforms all the state of the art methods in terms of different accuracy measures (some of them are brand new), demonstrating also a strong robustness to noise and versatility in recognizing groups of various cardinality.Comment: 32 pages, submitted to PLOS On

Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network

Accurate lane localization and lane change detection are crucial in advanced driver assistance systems and autonomous driving systems for safer and more efficient trajectory planning. Conventional localization devices such as Global Positioning System only provide road-level resolution for car navigation, which is incompetent to assist in lane-level decision making. The state of art technique for lane localization is to use Light Detection and Ranging sensors to correct the global localization error and achieve centimeter-level accuracy, but the real-time implementation and popularization for LiDAR is still limited by its computational burden and current cost. As a cost-effective alternative, vision-based lane change detection has been highly regarded for affordable autonomous vehicles to support lane-level localization. A deep learning-based computer vision system is developed to detect the lane change behavior using the images captured by a front-view camera mounted on the vehicle and data from the inertial measurement unit for highway driving. Testing results on real-world driving data have shown that the proposed method is robust with real-time working ability and could achieve around 87% lane change detection accuracy. Compared to the average human reaction to visual stimuli, the proposed computer vision system works 9 times faster, which makes it capable of helping make life-saving decisions in time

The State-of-the-art of Coordinated Ramp Control with Mixed Traffic Conditions

Ramp metering, a traditional traffic control strategy for conventional vehicles, has been widely deployed around the world since the 1960s. On the other hand, the last decade has witnessed significant advances in connected and automated vehicle (CAV) technology and its great potential for improving safety, mobility and environmental sustainability. Therefore, a large amount of research has been conducted on cooperative ramp merging for CAVs only. However, it is expected that the phase of mixed traffic, namely the coexistence of both human-driven vehicles and CAVs, would last for a long time. Since there is little research on the system-wide ramp control with mixed traffic conditions, the paper aims to close this gap by proposing an innovative system architecture and reviewing the state-of-the-art studies on the key components of the proposed system. These components include traffic state estimation, ramp metering, driving behavior modeling, and coordination of CAVs. All reviewed literature plot an extensive landscape for the proposed system-wide coordinated ramp control with mixed traffic conditions.Comment: 8 pages, 1 figure, IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE - ITSC 201

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs