Isovist Analyst - An Arcview extension for planning visual surveillance

7-11 August, 2006, San Diego, CA, USA. Visual Surveillance e.g. CCTV, is now an essential part of the urban infrastructure in modern cities. One of the primary aims in visual surveillance is to ensure a maximum visual coverage of an area with the least number of visual surveillance installations, which is a NP-Hard maximal coverage problem. The planning of visual surveillance is a highly sensitive and costly task that has traditionally been done with a gut-feel process of establishing sight lines in CAD software. This paper demonstrates the ArcView extension Isovist Analyst, which automatically identifies a minimal number of potential visual surveillance sites that ensure complete visual coverage of an area. The paper proposes a Stochastical Rank and Overlap Elimination (S-ROPE) method, which iteratively identifies the optimal visual surveillance sites. S-ROPE method is essentially based on a greedy search technique, which has been improved by a combination of selective sampling strategy and random initialisation

Optimized sensor placement for dependable roadside infrastructures

We present a multi-stage optimization method for efficient sensor deployment in traffic surveillance scenarios. Based on a genetic optimization scheme, our algorithm places an optimal number of roadside sensors to obtain full road coverage in the presence of obstacles and dynamic occlusions. The efficiency of the procedure is demonstrated for selected, realistic road sections. Our analysis helps to leverage the economic feasibility of distributed infrastructure sensor networks with high perception quality.Comment: 6 pages, 5 figures; IEEE Intelligent transportation systems conference 201

CCTV placement in Gaborone City, Botswana: A critical review through the lens of Situational Crime Prevention theory

Public surveillance technology through the installation of Closed-Circuit Television cameras (CCTV) has been widely acknowledged as a tool for monitoring population movements and preventing crime. Based on this technological value, the installation of CCTV cameras has become a growing trend in many cities globally. The year 2018 will be remembered as the year when CCTV cameras were first installed in the city of Gaborone, Botswana, and its surrounding villages for purposes of detecting criminal activities and preventing crime. The value of CCTV cameras in preventing crime and as an investigative tool has been an area of interest among researchers. Among scholarly studies on this field, the focus has been on the effectiveness of CCTV cameras in preventing crime and their value as an investigative tool. Since CCTV cameras have just been installed in the city of Gaborone, it may be too early to evaluate the extent to which they effectively prevent crime in this city. The purpose of this study is to document the use of public surveillance cameras in Gaborone and its surrounding areas and assess their geographic placement in light of the principles of Situational Crime Prevention (SCP) theory. Using data collected through site observation and key informant interviews, we argue for a rigorous review and assessment of the current installation and placement of CCTV cameras in Gaborone city and further scholarly study to measure and evaluate the effectiveness of CCTV camera use for crime prevention in this city

Towards optimal sensor deployment for location tracking in smart home

International audienceAmbient Assisted Living (AAL) aims to ease the daily living and working environmentfor disabled/elderly peopleat home. AAL use information and communication technology based on sensors data. These sensors are generally placed randomly without taking into account the layout of buildings and rooms. In this paper, we develop a mathematical model foroptimal sensor placement in order (i) to optimize the sensor number with regard to room features, (ii) to ensure a reliability level in sensor networkconsidering a sensor failure rate. This placement ensures the targettracking in smart home sinceoptimizing sensorplacement allow us to distinguish different zonesand consequently, to identify the target location, according to the activated sensors

Spatio-temporal coverage optimization of sensor networks

Les réseaux de capteurs sont formés d’un ensemble de dispositifs capables de prendre individuellement des mesures d’un environnement particulier et d’échanger de l’information afin d’obtenir une représentation de haut niveau sur les activités en cours dans la zone d’intérêt. Une telle détection distribuée, avec de nombreux appareils situés à proximité des phénomènes d’intérêt, est pertinente dans des domaines tels que la surveillance, l’agriculture, l’observation environnementale, la surveillance industrielle, etc. Nous proposons dans cette thèse plusieurs approches pour effectuer l’optimisation des opérations spatio-temporelles de ces dispositifs, en déterminant où les placer dans l’environnement et comment les contrôler au fil du temps afin de détecter les cibles mobiles d’intérêt. La première nouveauté consiste en un modèle de détection réaliste représentant la couverture d’un réseau de capteurs dans son environnement. Nous proposons pour cela un modèle 3D probabiliste de la capacité de détection d’un capteur sur ses abords. Ce modèle inègre également de l’information sur l’environnement grâce à l’évaluation de la visibilité selon le champ de vision. À partir de ce modèle de détection, l’optimisation spatiale est effectuée par la recherche du meilleur emplacement et l’orientation de chaque capteur du réseau. Pour ce faire, nous proposons un nouvel algorithme basé sur la descente du gradient qui a été favorablement comparée avec d’autres méthodes génériques d’optimisation «boites noires» sous l’aspect de la couverture du terrain, tout en étant plus efficace en terme de calculs. Une fois que les capteurs placés dans l’environnement, l’optimisation temporelle consiste à bien couvrir un groupe de cibles mobiles dans l’environnement. D’abord, on effectue la prédiction de la position future des cibles mobiles détectées par les capteurs. La prédiction se fait soit à l’aide de l’historique des autres cibles qui ont traversé le même environnement (prédiction à long terme), ou seulement en utilisant les déplacements précédents de la même cible (prédiction à court terme). Nous proposons de nouveaux algorithmes dans chaque catégorie qui performent mieux ou produits des résultats comparables par rapport aux méthodes existantes. Une fois que les futurs emplacements de cibles sont prédits, les paramètres des capteurs sont optimisés afin que les cibles soient correctement couvertes pendant un certain temps, selon les prédictions. À cet effet, nous proposons une méthode heuristique pour faire un contrôle de capteurs, qui se base sur les prévisions probabilistes de trajectoire des cibles et également sur la couverture probabiliste des capteurs des cibles. Et pour terminer, les méthodes d’optimisation spatiales et temporelles proposées ont été intégrées et appliquées avec succès, ce qui démontre une approche complète et efficace pour l’optimisation spatio-temporelle des réseaux de capteurs.Sensor networks consist in a set of devices able to individually capture information on a given environment and to exchange information in order to obtain a higher level representation on the activities going on in the area of interest. Such a distributed sensing with many devices close to the phenomena of interest is of great interest in domains such as surveillance, agriculture, environmental monitoring, industrial monitoring, etc. We are proposing in this thesis several approaches to achieve spatiotemporal optimization of the operations of these devices, by determining where to place them in the environment and how to control them over time in order to sense the moving targets of interest. The first novelty consists in a realistic sensing model representing the coverage of a sensor network in its environment. We are proposing for that a probabilistic 3D model of sensing capacity of a sensor over its surrounding area. This model also includes information on the environment through the evaluation of line-of-sight visibility. From this sensing model, spatial optimization is conducted by searching for the best location and direction of each sensor making a network. For that purpose, we are proposing a new algorithm based on gradient descent, which has been favourably compared to other generic black box optimization methods in term of performance, while being more effective when considering processing requirements. Once the sensors are placed in the environment, the temporal optimization consists in covering well a group of moving targets in the environment. That starts by predicting the future location of the mobile targets detected by the sensors. The prediction is done either by using the history of other targets who traversed the same environment (long term prediction), or only by using the previous displacements of the same target (short term prediction). We are proposing new algorithms under each category which outperformed or produced comparable results when compared to existing methods. Once future locations of targets are predicted, the parameters of the sensors are optimized so that targets are properly covered in some future time according to the predictions. For that purpose, we are proposing a heuristics for making such sensor control, which deals with both the probabilistic targets trajectory predictions and probabilistic coverage of sensors over the targets. In the final stage, both spatial and temporal optimization method have been successfully integrated and applied, demonstrating a complete and effective pipeline for spatiotemporal optimization of sensor networks

Jointly Optimizing Placement and Inference for Beacon-based Localization

The ability of robots to estimate their location is crucial for a wide variety of autonomous operations. In settings where GPS is unavailable, measurements of transmissions from fixed beacons provide an effective means of estimating a robot's location as it navigates. The accuracy of such a beacon-based localization system depends both on how beacons are distributed in the environment, and how the robot's location is inferred based on noisy and potentially ambiguous measurements. We propose an approach for making these design decisions automatically and without expert supervision, by explicitly searching for the placement and inference strategies that, together, are optimal for a given environment. Since this search is computationally expensive, our approach encodes beacon placement as a differential neural layer that interfaces with a neural network for inference. This formulation allows us to employ standard techniques for training neural networks to carry out the joint optimization. We evaluate this approach on a variety of environments and settings, and find that it is able to discover designs that enable high localization accuracy.Comment: Appeared at 2017 International Conference on Intelligent Robots and Systems (IROS

Deployment, Coverage And Network Optimization In Wireless Video Sensor Networks For 3D Indoor Monitoring

As a result of extensive research over the past decade or so, wireless sensor networks (wsns) have evolved into a well established technology for industry, environmental and medical applications. However, traditional wsns employ such sensors as thermal or photo light resistors that are often modeled with simple omni-directional sensing ranges, which focus only on scalar data within the sensing environment. In contrast, the sensing range of a wireless video sensor is directional and capable of providing more detailed video information about the sensing field. Additionally, with the introduction of modern features in non-fixed focus cameras such as the pan, tilt and zoom (ptz), the sensing range of a video sensor can be further regarded as a fan-shape in 2d and pyramid-shape in 3d. Such uniqueness attributed to wireless video sensors and the challenges associated with deployment restrictions of indoor monitoring make the traditional sensor coverage, deployment and networked solutions in 2d sensing model environments for wsns ineffective and inapplicable in solving the wireless video sensor network (wvsn) issues for 3d indoor space, thus calling for novel solutions. In this dissertation, we propose optimization techniques and develop solutions that will address the coverage, deployment and network issues associated within wireless video sensor networks for a 3d indoor environment. We first model the general problem in a continuous 3d space to minimize the total number of required video sensors to monitor a given 3d indoor region. We then convert it into a discrete version problem by incorporating 3d grids, which can achieve arbitrary approximation precision by adjusting the grid granularity. Due in part to the uniqueness of the visual sensor directional sensing range, we propose to exploit the directional feature to determine the optimal angular-coverage of each deployed visual sensor. Thus, we propose to deploy the visual sensors from divergent directional angles and further extend k-coverage to ``k-angular-coverage\u27\u27, while ensuring connectivity within the network. We then propose a series of mechanisms to handle obstacles in the 3d environment. We develop efficient greedy heuristic solutions that integrate all these aforementioned considerations one by one and can yield high quality results. Based on this, we also propose enhanced depth first search (dfs) algorithms that can not only further improve the solution quality, but also return optimal results if given enough time. Our extensive simulations demonstrate the superiority of both our greedy heuristic and enhanced dfs solutions. Finally, this dissertation discusses some future research directions such as in-network traffic routing and scheduling issues