A socio-economic approach to online vision graph generation and handover in distributed smart camera networks

In this paper we propose an approach based on self-interested autonomous cameras, which exchange responsibility for tracking objects in a market mechanism, in order to maximise their own utility. A novel ant-colony inspired mechanism is used to grow the vision graph during runtime, which may then be used to optimise communication between cameras. The key benefits of our completely decentralised approach are on the one hand generating the vision graph online which permits the addition and removal cameras to the network during runtime and on the other hand relying only on local information, increasing the robustness of the system. Since our market-based approach does not rely on a priori topology information, the need for any multi-camera calibration can be avoided. © 2011 IEEE.

A catalytic reactor for the trapping of free radicals from gas phase oxidation reactions

A catalytic reactor for the trapping of free radicals originating from gas phase catalytic reactions is described and discussed. Radical trapping and identification were initially carried out using a known radical generator such as dicumyl peroxide. The trapping of radicals was further demonstrated by investigating genuine radical oxidation processes, e.g., benzaldehyde oxidation over manganese and cobalt salts. The efficiency of the reactor was finally proven by the partial oxidation of cyclohexane over MoO3, Cr2O3, and WO3, which allowed the identification of all the radical intermediates responsible for the formation of the products cyclohexanol and cyclohexanone. Assignment of the trapped radicals was carried out using spin trapping technique and X -band electron paramagnetic resonance spectroscopy. © 2010 American Institute of Physics.

Socio-economic vision graph generation and handover in distributed smart camera networks

In this article we present an approach to object tracking handover in a network of smart cameras, based on self-interested autonomous agents, which exchange responsibility for tracking objects in a market mechanism, in order to maximise their own utility. A novel ant-colony inspired mechanism is used to learn the vision graph, that is, the camera neighbourhood relations, during runtime, which may then be used to optimise communication between cameras. The key benefits of our completely decentralised approach are on the one hand generating the vision graph online, enabling efficient deployment in unknown scenarios and camera network topologies, and on the other hand relying only on local information, increasing the robustness of the system. Since our market-based approach does not rely on a priori topology information, the need for any multicamera calibration can be avoided. We have evaluated our approach both in a simulation study and in network of real distributed smart cameras.

Improved adaptivity and robustness in decentralised multi-camera networks

In this paper we present increased adaptivity and robustness in distributed object tracking by multi-camera networks using a socio-economic mechanism for learning the vision graph. To build-up the vision graph autonomously within a distributed smart-camera network, we use an ant-colony inspired mechanism, which exchanges responsibility for tracking objects using Vickrey auctions. Employing the learnt vision graph allows the system to optimise its communication continuously. Since distributed smart camera networks are prone to uncertainties in individual cameras, such as failures or changes in extrinsic parameters, the vision graph should be sufficiently robust and adaptable during runtime to enable seamless tracking and optimised communication. To better reflect real smart-camera platforms and networks, we consider that communication and handover are not instantaneous, and that cameras may be added, removed or their properties changed during runtime. Using our dynamic socio-economic approach, the network is able to continue tracking objects well, despite all these uncertainties, and in some cases even with improved performance. This demonstrates the adaptivity and robustness of our approach.

CamSim:a distributed smart camera network simulator

Smart cameras allow pre-processing of video data on the camera instead of sending it to a remote server for further analysis. Having a network of smart cameras allows various vision tasks to be processed in a distributed fashion. While cameras may have different tasks, we concentrate on distributed tracking in smart camera networks. This application introduces various highly interesting problems. Firstly, how can conflicting goals be satisfied such as cameras in the network try to track objects while also trying to keep communication overhead low? Secondly, how can cameras in the network self adapt in response to the behavior of objects and changes in scenarios, to ensure continued efficient performance? Thirdly, how can cameras organise themselves to improve the overall network's performance and efficiency? This paper presents a simulation environment, called CamSim, allowing distributed self-adaptation and self-organisation algorithms to be tested, without setting up a physical smart camera network. The simulation tool is written in Java and hence allows high portability between different operating systems. Relaxing various problems of computer vision and network communication enables a focus on implementing and testing new self-adaptation and self-organisation algorithms for cameras to use.

Learning to be different:heterogeneity and efficiency in distributed smart camera networks

In this paper we study the self-organising behaviour of smart camera networks which use market-based handover of object tracking responsibilities to achieve an efficient allocation of objects to cameras. Specifically, we compare previously known homogeneous configurations, when all cameras use the same marketing strategy, with heterogeneous configurations, when each camera makes use of its own, possibly different marketing strategy. Our first contribution is to establish that such heterogeneity of marketing strategies can lead to system wide outcomes which are Pareto superior when compared to those possible in homogeneous configurations. However, since the particular configuration required to lead to Pareto efficiency in a given scenario will not be known in advance, our second contribution is to show how online learning of marketing strategies at the individual camera level can lead to high performing heterogeneous configurations from the system point of view, extending the Pareto front when compared to the homogeneous case. Our third contribution is to show that in many cases, the dynamic behaviour resulting from online learning leads to global outcomes which extend the Pareto front even when compared to static heterogeneous configurations. Our evaluation considers results obtained from an open source simulation package as well as data from a network of real cameras. © 2013 IEEE.

Resource-aware configuration in smart camera networks

A recent trend in smart camera networks is that they are able to modify the functionality during runtime to better reflect changes in the observed scenes and in the specified monitoring tasks. In this paper we focus on different configuration methods for such networks. A configuration is given by three components: (i) a description of the camera nodes, (ii) a specification of the area of interest by means of observation points and the associated monitoring activities, and (iii) a description of the analysis tasks. We introduce centralized, distributed and proprioceptive configuration methods and compare their properties and performance. © 2012 IEEE.

Optical trapping with superfocused high-M2 laser diode beam

Many applications of high-power laser diodes demand tight focusing. This is often not possible due to the multimode nature of semiconductor laser radiation possessing beam propagation parameter M2 values in double-digits. We propose a method of 'interference' superfocusing of high-M2 diode laser beams with a technique developed for the generation of Bessel beams based on the employment of an axicon fabricated on the tip of a 100 μm diameter optical fiber with highprecision direct laser writing. Using axicons with apex angle 140º and rounded tip area as small as 10 μm diameter, we demonstrate 2-4 μm diameter focused laser 'needle' beams with approximately 20 μm propagation length generated from multimode diode laser with beam propagation parameter M2=18 and emission wavelength of 960 nm. This is a few-fold reduction compared to the minimal focal spot size of 11 μm that could be achieved if focused by an 'ideal' lens of unity numerical aperture. The same technique using a 160º axicon allowed us to demonstrate few-μm-wide laser 'needle' beams with nearly 100 μm propagation length with which to demonstrate optical trapping of 5-6 μm rat blood red cells in a water-heparin solution. Our results indicate the good potential of superfocused diode laser beams for applications relating to optical trapping and manipulation of microscopic objects including living biological objects with aspirations towards subsequent novel lab-on-chip configurations.

Optical trapping with Bessel beams generated from semiconductor lasers

In this paper, we study generation of Bessel beams from semiconductor lasers with high beam propagation parameter M2 and their utilization for optical trapping and manipulation of microscopic particles including living cells. The demonstrated optical tweezing with diodegenerated Bessel beams paves the way to replace their vibronic-generated counterparts for a range of applications towards novel lab-on-a-chip configurations.