Dynamic Reconfiguration in Camera Networks: A Short Survey

There is a clear trend in camera networks towards enhanced functionality and flexibility, and a fixed static deployment is typically not sufficient to fulfill these increased requirements. Dynamic network reconfiguration helps to optimize the network performance to the currently required specific tasks while considering the available resources. Although several reconfiguration methods have been recently proposed, e.g., for maximizing the global scene coverage or maximizing the image quality of specific targets, there is a lack of a general framework highlighting the key components shared by all these systems. In this paper we propose a reference framework for network reconfiguration and present a short survey of some of the most relevant state-of-the-art works in this field, showing how they can be reformulated in our framework. Finally we discuss the main open research challenges in camera network reconfiguration

Online Audio-Visual Multi-Source Tracking and Separation: A Labeled Random Finite Set Approach

The dissertation proposes an online solution for separating an unknown and time-varying number of moving sources using audio and visual data. The random finite set framework is used for the modeling and fusion of audio and visual data. This enables an online tracking algorithm to estimate the source positions and identities for each time point. With this information, a set of beamformers can be designed to separate each desired source and suppress the interfering sources

ADAPTIVE IMAGE ENHANCEMENT MODEL FOR THE ROBOT VISION SYSTEM

Robotics is one of the important trends in the current development of science and technology. Most modern robots and drones have their own vision system, including a video camera, which they use to take digital photos and video streams. These data are used to analyze the situation in the robot's camera field of view, as well as to determine a real-time robot's behavior algorithm. In this regard, the novelty of the paper is special polynomial mathematical model and method for adaptive gradational correction of a digital image. The proposed model and method make it possible to independently adjust to brightness scales and image formats and optimally perform gradational image correction in various lighting conditions. Thus, ensuring the efficiency of the entire subsequent cycle of image analysis in the robot's vision system. In addition, the paper presents the results of numerous experiments of such gradational correction for images of various classes, as well as conditions of reduced and increased levels of illumination of the field of view objects. Conclusions and recommendations are given regarding the practical application of the proposed model and method

Image Space Coverage Model for Deployment of Multi-Camera Networks

When it comes to visual sensor networks deployment and optimization, modeling the coverage of a given camera network is a vital step. Due to many complex parameters and criteria that governs coverage quality of a given visual network, modeling such coverage accurately and efficiently represents a real challenge. This thesis explores the idea of simplifying the mathematical interpretation that describes a given visual sensor without incurring a cost on coverage measurement accuracy. In this thesis, coverage criteria are described in image space, in contrast to some of the more advanced models found in literature, that are formulated in 3D space, which in turn will have a direct impact on efficiency and time cost. In addition, this thesis also proposes a novel sensor deployment approach that examines the surface topology of the target object to be covered by means of a mesh segmentation algorithm, which is that a different way to tackle the problem other than the exhaustive search methods employed in the examined literature. There are two main contributions in this thesis. Firstly, a new coverage model that takes partial occlusion criterion into account is proposed, which is shown to be more accurate and more efficient than the competition. Next, a new sensor deployment method was presented that takes the target object shape topological properties into account, an approach that is to the best of our knowledge, was not attempted in literature before at the time of publication. This thesis attempts to support all of claims made above, the proposed model is validated and compared to an existing state of art coverage model. In addition, simulations and experiments were carried out to demonstrate the accuracy and time cost efficiency of the proposed work

Power Management in Sensing Subsystem of Wireless Multimedia Sensor Networks

A wireless sensor network consists of sensor nodes deployed over a geographical area for monitoring physical phenomena like temperature, humidity, vibrations, seismic events, and so on. Typically, a sensor node is a tiny device that includes three basic components: a sensing subsystem for data acquisition from the physical surrounding environment, a processing subsystem for local data processing and storage, and a wireless communication subsystem for data transmission. In addition, a power source supplies the energy needed by the device to perform the programmed task. This power source often consists of a battery with a limited energy budget. In addition, it is usually impossible or inconvenient to recharge the battery, because nodes are deployed in a hostile or unpractical environment. On the other hand, the sensor network should have a lifetime long enough to fulfill the application requirements. Accordingly, energy conservation in nodes and maximization of network lifetime are commonly recognized as a key challenge in the design and implementation of WSNs. Experimental measurements have shown that generally data transmission is very expensive in terms of energy consumption, while data processing consumes significantly less (Raghunathan et al., 2002). The energy cost of transmitting a single bit of information is approximately the same as that needed for processing a thousand operations in a typical sensor node (Pottie & Kaiser, 2000). The energy consumption of the sensing subsystem depends on the specific sensor type. In some cases of scalar sensors, it is negligible with respect to the energy consumed by the processing and, above all, the communication subsystems. In other cases, the energy expenditure for data sensing may be comparable to, or even greater (in the case of multimedia sensing) than the energy needed for data transmission. In general, energy-saving techniques focus on two subsystems: the communication subsystem (i.e., energy management is taken into account in the operations of each single node, as well as in the design of networking protocols), and the sensing subsystem (i.e., techniques are used to reduce the amount or frequency of energy-expensive samples).Postprint (published version

Quality-of-Information Aware Sensing Node Characterisation for Optimised Energy Consumption in Visual Sensor Networks

Energy consumption is one of the primary concerns in a resource constrained visual sensor network (VSN) with wireless transceiving capability. The existing VSN design solutions under particular resource constrained scenarios are application-specific, whereas the degree of sensitivity of the resource constraints varies from one application to another. This limits the implementation of the existing energy efficient solutions within a VSN node, which may be considered to be a part of a heterogeneous network. This thesis aims to resolve the energy consumption issues faced within VSNs because of their resource constrained nature by proposing energy efficient solutions for sensing nodes characterisation. The heterogeneity of image capture and processing within a VSN can be adaptively reflected with a dynamic field-of-view (FoV) realisation. This is expected to allow the implementation of a generalised energy efficient solution that will adapt with the heterogeneity of the network. In this thesis, a FoV characterisation framework is proposed, which can assist design engineers during the pre-deployment phase in developing energy efficient VSNs. The proposed FoV characterisation framework provides efficient solutions for: 1) selecting suitable sensing range; 2) maximising spatial coverage; 3) minimising the number of required nodes; and 4) adaptive task classification. The task classification scheme proposed in this thesis exploits heterogeneity of the network and leads to an optimal distribution of tasks between visual sensing nodes. Soft decision criteria is exploited, and it is observed that for a given detection reliability, the proposed FoV characterisation framework provides energy efficient solutions which can be implemented within heterogeneous networks. In the post-deployment phase, the energy efficiency of a VSN for a given level of reliability can be enhanced by reconfiguring its nodes dynamically to achieve optimal configurations. Considering the dynamic realisation of quality-of-information (QoI), a strategy is devised for selecting suitable configurations of visual sensing nodes to reduce redundant visual content prior to transmission without sacrificing the expected information retrieval reliability. By incorporating QoI awareness using peak signal-to-noise ratio-based representative metric, the distributed nature of the proposed self-reconfiguration scheme accelerates the decision making process. This thesis also proposes a unified framework for node classification and dynamic self-reconfiguration in VSNs. For a given application, the unified framework provides a feasible solution to classify and reconfigure visual sensing nodes based on their FoV by exploiting the heterogeneity of targeted QoI within the sensing region. From the results, it is observed that for the second degree of heterogeneity in targeted QoI, the unified framework outperforms its existing counterparts and results in up to 72% energy savings with as low as 94% reliability. Within the context of resource constrained VSNs, the substantial energy savings achieved by the proposed unified framework can lead to network lifetime enhancement. Moreover, the reliability analysis demonstrates suitability of the unified framework for applications that need a desired level of QoI