An Introduction to Distributed Smart Cameras

Rinner, Bernhard; Wolf, Wayne

doi:10.1109/jproc.2008.928742

Cited by 150 publications

(90 citation statements)

References 50 publications

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“…(12) guarantees the correctness of the speed computation F c v ; Eqn. (13) ensures that all voxels inside the narrow band Ω are updated. With the satisfaction of (12) and (13), the "free" voxels are distributed with the consideration of load balance among cameras with the Algorithm (4).…”

Section: Job Distribution Schemementioning

confidence: 99%

“…These "smart cameras" can form a network to collaboratively monitor, track and analyze the scenes of interest. This area have drawn a lot of attention in both academia and industry over the past years (see [1] [11] and [13] for an overview). However compared with the maturity and availability of the camera network hardware, the software capable of fully utilizing the huge amount of visual information is greatly under-developed.…”

mentioning

confidence: 99%

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Distributed volumetric scene geometry reconstruction with a network of distributed smart cameras

Liu

Kang

Tarel

et al. 2009

2009 IEEE Conference on Computer Vision and Pattern Recognition

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Central to many problems in scene understanding based on using a network of tens, hundreds 1. An Overview of the System MotivationWith the recent development of cheap and powerful visual sensors, wireless chips and embedded systems, cameras have enough computing power to do some on-board "smart" processing. These "smart cameras" can form a network to collaboratively monitor, track and analyze the scenes of interest. This area have drawn a lot of attention in both academia and industry over the past years (see [1] [11] and [13] for an overview). However compared with the maturity and availability of the camera network hardware, the software capable of fully utilizing the huge amount of visual information is greatly under-developed. This has become the bottleneck for the wide deployment of the smart camera network (also called visual sensor network, VSN). There is an obvious demand to synchronize the recent development of vision algorithms with the development of the visual sensor network hardware. Our paper presents a completely new and natural approach to 3D reconstruction within a smart camera network.

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Section: Job Distribution Schemementioning

confidence: 99%

mentioning

confidence: 99%

Distributed volumetric scene geometry reconstruction with a network of distributed smart cameras

Liu

Kang

Tarel

et al. 2009

2009 IEEE Conference on Computer Vision and Pattern Recognition

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“…Besides building applications from locally running blocks, applications can also subscribe to output generated by blocks running on a remote node. This feature is required if multiple cameras work on a common problem such as multi-camera object tracking or when there 1 BeagleBoard Website: http://www.beagleboard.org (June 2009) is insufficient computing power on a node and the workload needs to be distributed.…”

Section: System Architecturementioning

confidence: 99%

“…Smart cameras are embedded systems doing on-board image analysis and being capable of delivering high level event descriptions [1]. Over the last few years, a lot of research was performed on wireless sensor networks (WSNs) for e.g.…”

Section: Introductionmentioning

confidence: 99%

Power aware communication in wireless pervasive smart camera networks

Winkler

Rinner

2009

2009 International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)

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Abstract-Visual sensor networks bring together the research domains of smart cameras and wireless sensor networks. Focusing on power consumption and long-term deployments, typical sensor nodes come with limited computing power and low communication performance. A smart camera that performs onboard video analysis and exchanges data with adjacent nodes has higher computation and communication requirements. Therefore, a main challenge in visual sensor networks is to find a tradeoff between performance and power consumption. This work presents a camera prototype equipped with a low-and a high-performance radio allowing to trade communication performance for power consumption by selecting the appropriate device. Radio power consumption is evaluated for different modes of operation and a power model for clusters of dual-radio cameras is presented.

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“…Furthermore, an emerging privacy threat is posed by camera-equipped unmanned aerial vehicles (UAVs) also known as drones [2,10,16,48]. Traditional CCTV (closed-circuit television) and other old-fashioned surveillance camera systems are continuously replaced recently by visual sensor networks (VSNs) which consist of smart cameras [43,44]. Due to networking and on-board processing capabilities of the above mentioned visual data capturing devices, sophisticated artificial vision tasks can be performed.…”

Section: Introductionmentioning

confidence: 99%

Privacy protection vs. utility in visual data

Erdélyi

Winkler

Rinner

2017

Multimed Tools Appl

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Ubiquitous and networked sensors impose a huge challenge for privacy protection which has become an emerging problem of modern society. Protecting the privacy of visual data is particularly important due to the omnipresence of cameras, and various protection mechanisms for captured images and videos have been proposed. This paper introduces an objective evaluation framework in order to assess such protection methods. Visual privacy protection is typically realised by obfuscating sensitive image regions which often results in some loss of utility. Our evaluation framework assesses the achieved privacy protection and utility by comparing the performance of standard computer vision tasks, such as object recognition, detection and tracking on protected and unprotected visual data. The proposed framework extends the traditional frame-by-frame evaluation approach by introducing two new approaches based on aggregated and fused frames. We demonstrate our framework on eight differently protected video-sets and measure the trade-off between the improved privacy protection due to obfuscating captured image data and the degraded utility of the visual data. Results provided by our objective evaluation method are compared with an available state-of-the-art subjective study of these eight protection techniques.

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An Introduction to Distributed Smart Cameras

Cited by 150 publications

References 50 publications

Distributed volumetric scene geometry reconstruction with a network of distributed smart cameras

Distributed volumetric scene geometry reconstruction with a network of distributed smart cameras

Power aware communication in wireless pervasive smart camera networks

Privacy protection vs. utility in visual data

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