A high resolution smart camera with GigE Vision extension for surveillance applications

Norouznezhad, Ehsan; Bigdeli, Abbas; Postula, Adam; Lovell, Brian C.

doi:10.1109/icdsc.2008.4635711

Cited by 28 publications

(10 citation statements)

References 16 publications

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“…Hence, it typically concerns development of FPGA-based smart cameras and other FPGA-based hardware solutions. Examples include a custom-designed smart camera with GigE Vision interface and FPGA-based processing [7], a multi-camera FPGA-and GigE Vision-based image processing system [8], and a custom FPGA-based solution for acquiring images from GigE Vision interface (by bypassing the machine's CPUs) and direct writing to RAM [9]. This paper, conversely, focuses on implementation of image acquisition function on a general-purpose machine, with the ability to spread-out the processing function over a number of networked nodes.…”

Section: Image Acquisition Processmentioning

confidence: 99%

Flexible Image Acquisition Service for Distributed Robotic Systems

Semeniuta¹,

Falkman²

2018

2018 Second IEEE International Conference on Robotic Computing (IRC)

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Abstract-The widespread use vision systems in robotics introduces a number of challenges related to management of image acquisition and image processing tasks, as well as their coupling to the robot control function. With the proliferation of more distributed setups and flexible robotic architectures, the workflow of image acquisition needs to support a wider variety of communication styles and application scenarios. This paper presents FxIS, a flexible image acquisition service targeting distributed robotic systems with event-based communication.The principal idea a FxIS is in composition of a number of execution threads with a set of concurrent data structures, supporting acquisition from multiple cameras that is closely synchronized in time, both between the cameras and with the request timestamp.

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Section: Image Acquisition Processmentioning

confidence: 99%

Flexible Image Acquisition Service for Distributed Robotic Systems

Semeniuta¹,

Falkman²

2018

2018 Second IEEE International Conference on Robotic Computing (IRC)

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“…On the other side, FPGA-based architectures which offering more massive computing capability than GPPs have been studied for implementing image processing applications on smart camera nodes [4]. However, if the introduction of FPGAs can address some performance issues in smart camera networks, it introduces new challenges concerning programmability of nodes, hardware abstraction and network management.…”

Section: Introductionmentioning

confidence: 99%

Distributed FPGA-based smart camera architecture for computer vision applications

Bourrasset¹,

Maggianiy²,

Sérot³

et al. 2013

2013 Seventh International Conference on Distributed Smart Cameras (ICDSC)

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International audienceSmart camera networks (SCN) raise challenging issues in many fields of research, including vision processing, communication protocols, distributed algorithms or power management. Furthermore, application logic in SCN is not centralized but spread among network nodes meaning that each node must have to process images to extract significant features, and aggregate data to understand the surrounding environment. In this context, smart camera have first embedded general purpose processor (GPP) for image processing. Since image resolution increases, GPPs have reached their limit to maintain real-time processing constraint. More recently, FPGA-based platforms have been studied for their massive parallelism capabilities. This paper present our new FPGA-based smart camera platform supporting cooperation between nodes and run-time updatable image processing. The architecture is based on a full reconfigurable pipeline driven by a softcore

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“…This kind of architecture offers high level programming and modern embedded processors provide good performances but cannot meet real-time processing constraints when image resolution increases. On the other side, FPGA-based architectures, offering more massive computing capability have been the subject of tremendous attention for implementing image processing applications on smart camera nodes [3], [4]. However, if the introduction of FPGAs can address some performance issues in smart camera networks, it introduces new challenges concerning node programmability, hardware abstraction, dynamic configuration and network management.…”

Section: Introductionmentioning

confidence: 99%

DreamCAM: A FPGA-based platform for smart camera networks

Bourrasset

Maggiani

Sérot

et al. 2013

2013 Seventh International Conference on Distributed Smart Cameras (ICDSC)

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International audience—The main challenges in smart camera networks come from the limited capacity of network communications. Indeed, wireless protocols such as the IEEE 802.15.4 protocol target low data rate, low power consumption and low cost wireless networking in order to fit the requirements of sensor networks. Since nodes more and more often integrate image sensors, network bandwidth has become a strong limiting factor in application deployment. This means that data must be processed at the node level before being sent on the network. In this context, FPGA-based platforms, supporting massive data parallelism, offer large opportunities for on-board processing. We present in this paper our FPGA-based smart camera platform, called DreamCam, which is able to autonomously exchange processed information on an Ethernet network

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A high resolution smart camera with GigE Vision extension for surveillance applications

Abstract: ABSTRACT

Cited by 28 publications

References 16 publications

Flexible Image Acquisition Service for Distributed Robotic Systems

Flexible Image Acquisition Service for Distributed Robotic Systems

Distributed FPGA-based smart camera architecture for computer vision applications

DreamCAM: A FPGA-based platform for smart camera networks

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