Trial by fire at San Francisco; the evidence of the camera. Pub. by National fire proofing company ...

Mode of access: Internet.

Camera magazine.

Mode of access: Internet.

The Camera.

Description based on: vol. 5, no. 1.

Guidelines for filming digital camera video clips for the assessment of gait and movement disorders by teleneurology

Digital still cameras capable of filming short video clips are readily available, but the quality of these recordings for telemedicine has not been reported. We performed a blinded study using four commonly available digital cameras. A simulated patient with a hemiplegic gait pattern was filmed by the same videographer in an identical, brightly lit indoor setting. Six neurologists viewed the blinded video clips on their PC and comparisons were made between cameras, between video clips recorded with and without a tripod, and between video clips filmed on high- or low-quality settings. Use of a tripod had a smaller effect than expected, while images taken on a high-quality setting were strongly preferred to those taken on a low-quality setting. Although there was some variability in video quality between selected cameras, all were of sufficient quality to identify physical signs such as gait and tremor. Adequate-quality video clips of movement disorders can be produced with low-cost cameras and transmitted by email for teleneurology purposes.

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.

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.