Conferences related to Smart Cameras

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2023 Annual International Conference of the IEEE Engineering in Medicine & Biology Conference (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted full papers will be peer reviewed. Accepted high quality papers will be presented in oral and poster sessions,will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE.


2020 IEEE International Conference on Image Processing (ICIP)

The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.


2020 IEEE International Conference on Robotics and Automation (ICRA)

The International Conference on Robotics and Automation (ICRA) is the IEEE Robotics and Automation Society’s biggest conference and one of the leading international forums for robotics researchers to present their work.


2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

The Conference focuses on all aspects of instrumentation and measurement science andtechnology research development and applications. The list of program topics includes but isnot limited to: Measurement Science & Education, Measurement Systems, Measurement DataAcquisition, Measurements of Physical Quantities, and Measurement Applications.


GLOBECOM 2020 - 2020 IEEE Global Communications Conference

IEEE Global Communications Conference (GLOBECOM) is one of the IEEE Communications Society’s two flagship conferences dedicated to driving innovation in nearly every aspect of communications. Each year, more than 2,900 scientific researchers and their management submit proposals for program sessions to be held at the annual conference. After extensive peer review, the best of the proposals are selected for the conference program, which includes technical papers, tutorials, workshops and industry sessions designed specifically to advance technologies, systems and infrastructure that are continuing to reshape the world and provide all users with access to an unprecedented spectrum of high-speed, seamless and cost-effective global telecommunications services.


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Periodicals related to Smart Cameras

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Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Broadcasting, IEEE Transactions on

Broadcast technology, including devices, equipment, techniques, and systems related to broadcast technology, including the production, distribution, transmission, and propagation aspects.


Circuits and Systems for Video Technology, IEEE Transactions on

Video A/D and D/A, display technology, image analysis and processing, video signal characterization and representation, video compression techniques and signal processing, multidimensional filters and transforms, analog video signal processing, neural networks for video applications, nonlinear video signal processing, video storage and retrieval, computer vision, packet video, high-speed real-time circuits, VLSI architecture and implementation for video technology, multiprocessor systems--hardware and software-- ...


Communications Magazine, IEEE

IEEE Communications Magazine was the number three most-cited journal in telecommunications and the number eighteen cited journal in electrical and electronics engineering in 2004, according to the annual Journal Citation Report (2004 edition) published by the Institute for Scientific Information. Read more at http://www.ieee.org/products/citations.html. This magazine covers all areas of communications such as lightwave telecommunications, high-speed data communications, personal communications ...


Computer

Computer, the flagship publication of the IEEE Computer Society, publishes peer-reviewed technical content that covers all aspects of computer science, computer engineering, technology, and applications. Computer is a resource that practitioners, researchers, and managers can rely on to provide timely information about current research developments, trends, best practices, and changes in the profession.


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Most published Xplore authors for Smart Cameras

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Xplore Articles related to Smart Cameras

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Using Smart Cameras to Localize Self-Assembling Modular Robots

2007 First ACM/IEEE International Conference on Distributed Smart Cameras, 2007

In order to realize the goal of self assembling or self reconfiguring modular robots the constituent modules in the system need to be able to gauge their position and orientation with respect to each other. This paper describes an approach to solving this localization problem by equipping each of the modules in the ensemble with a smart camera system. The ...


Image Processing for In-vehicle Smart Cameras

2006 IEEE Intelligent Vehicles Symposium, 2006

Smart cameras are increasingly being deployed in many automotive applications. The in-vehicle environment presents unique challenges to the camera functions and can significantly degrade the performance of smart cameras. In this paper, we present two image processing algorithms that address two of the most common challenges for in-vehicle smart cameras, namely exposure control and motion induced distortion. The proposed algorithms ...


PhD forum: Keypoints-based background model and foreground pedestrians extraction for future smart cameras

2009 Third ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC), 2009

In this paper, we present a method for background modeling using only keypoints, and detection of foreground moving pedestrians using background keypoints substraction followed by adaBoost classification of foreground keypoints. A first experimental evaluation shows very promising detection performances in real-time.


Smart Cameras and the Right to Privacy

Proceedings of the IEEE, 2008

This essay provides a matrix for use by researchers and system designers as a heuristic device to assess the likely legality of the deployment of a surveillance camera system. After presenting the matrix the essay considers examples in which smart camera technology might enhance the venues for deployment of surveillance cameras. Lastly, the article speculates about legal risks that may ...


Facial Recognition Technology on ELcore Semantic Processors for Smart Cameras

2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), 2019

This article introduces a solution to the problem of improving security in public places by analyzing the behaviour of people using smart cameras. The article describes the technologies for creating smart cameras for semantic image analysis based on the ELcore cores. The stages of semantic image analysis to detect and recognize faces are considered. The most resource- intensive algorithms are ...


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Educational Resources on Smart Cameras

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IEEE-USA E-Books

  • Using Smart Cameras to Localize Self-Assembling Modular Robots

    In order to realize the goal of self assembling or self reconfiguring modular robots the constituent modules in the system need to be able to gauge their position and orientation with respect to each other. This paper describes an approach to solving this localization problem by equipping each of the modules in the ensemble with a smart camera system. The paper describes one implementation of this scheme on a modular robotic system and discusses the results of a self assembly experiment.

  • Image Processing for In-vehicle Smart Cameras

    Smart cameras are increasingly being deployed in many automotive applications. The in-vehicle environment presents unique challenges to the camera functions and can significantly degrade the performance of smart cameras. In this paper, we present two image processing algorithms that address two of the most common challenges for in-vehicle smart cameras, namely exposure control and motion induced distortion. The proposed algorithms are very efficient and highly suitable for embedded real-time smart cameras. The algorithms were first developed for a real-time in-vehicle automatic license plate recognition smart camera but the techniques are also applicable to many other in-vehicle smart camera applications

  • PhD forum: Keypoints-based background model and foreground pedestrians extraction for future smart cameras

    In this paper, we present a method for background modeling using only keypoints, and detection of foreground moving pedestrians using background keypoints substraction followed by adaBoost classification of foreground keypoints. A first experimental evaluation shows very promising detection performances in real-time.

  • Smart Cameras and the Right to Privacy

    This essay provides a matrix for use by researchers and system designers as a heuristic device to assess the likely legality of the deployment of a surveillance camera system. After presenting the matrix the essay considers examples in which smart camera technology might enhance the venues for deployment of surveillance cameras. Lastly, the article speculates about legal risks that may confront smart camera technology as it becomes more sophisticated.

  • Facial Recognition Technology on ELcore Semantic Processors for Smart Cameras

    This article introduces a solution to the problem of improving security in public places by analyzing the behaviour of people using smart cameras. The article describes the technologies for creating smart cameras for semantic image analysis based on the ELcore cores. The stages of semantic image analysis to detect and recognize faces are considered. The most resource- intensive algorithms are identified and implemented on DSP-cores of ELcore. The general path of image processing on DSP-cores of ELcore for face detection and recognition is no longer than 32 ms. This parameter meets the requirements for processing video frames in real-time mode and can be used in smart cameras for security systems.

  • Improved Agent-Oriented Middleware for Distributed Smart Cameras

    In the recent past, much effort has been put into the development of distributed vision systems with smart cameras as key components. Smart cameras combine video sensing, processing and communication within a single embedded device and provide sufficient on-board infrastructure to carry out high-level video analysis tasks. Networks of smart cameras help to overcome some hard problems inherent to single-camera systems by providing multiple views of a scene. This paper reports on an improved, agent-oriented middleware for embedded smart cameras. Each image processing task is represented by an agent resident on a smart camera within the network. Agents are able to move from one camera to another as needed during run-time. An agent is comprised of the high-level application logic and the image processing algorithm which is executed on the processing unit. The presented middleware is also designed for distributed image processing where two or more cameras can cooperate for a single task. In the paper we discuss the requirements for such an agent- oriented middleware capable of supporting distributed image processing. Further, we describe the architecture of our middleware implementation. The evaluation of our current middleware implementation shows significant performance improvements compared to our previous Java-based implementation.

  • Object Detection, Tracking and Recognition for Multiple Smart Cameras

    Video cameras are among the most commonly used sensors in a large number of applications, ranging from surveillance to smart rooms for videoconferencing. There is a need to develop algorithms for tasks such as detection, tracking, and recognition of objects, specifically using distributed networks of cameras. The projective nature of imaging sensors provides ample challenges for data association across cameras. We first discuss the nature of these challenges in the context of visual sensor networks. Then, we show how real- world constraints can be favorably exploited in order to tackle these challenges. Examples of real-world constraints are (a) the presence of a world plane, (b) the presence of a three-dimiensional scene model, (c) consistency of motion across cameras, and (d) color and texture properties. In this regard, the main focus of this paper is towards highlighting the efficient use of the geometric constraints induced by the imaging devices to derive distributed algorithms for target detection, tracking, and recognition. Our discussions are supported by several examples drawn from real applications. Lastly, we also describe several potential research problems that remain to be addressed.

  • Integrating smart cameras into ZigBee

    Up to now, surveillance applications provided by Closed Circuit Television (CCTV) systems have been separated from core Building Automation Systems (BAS). The main reason is that technologies typically used in the field area of BAS provide limited network bandwidth. With the introduction of smart cameras, advanced image and on-the-spot pattern recognition become possible. As a result, it is no longer required to transmit a continuous video stream, since it is sufficient to inform client(s) only in case a suspicious event has happened. Since a simple notification (e.g., an alarm signal or a snapshot of the detected event) needs less network bandwidth, an integration into an existing BAS becomes feasible. Still, current BAS technologies do not provide native support for CCTV applications. For this reason, this paper presents an extension to the ZigBee Home Automation Profile. The feasibility of the proposed concept is underlined by a proof-of-concept implementation.

  • Smart cameras with real-time video object generation

    The paper presents a system for video object generation and selective encoding with applications in surveillance, mobile videophones, and the automotive industry. Object tracking and MPEG-4 compression are performed in real-time. The system belongs to a new generation of intelligent vision sensors called smart cameras, which execute autonomous vision tasks and report events and data to a remote base-station. A detection module signals the presence of an object of interest within the camera field of view, while the tracking part follows the target to generate temporal trajectories. The compression is MPEG-4 compliant and implements the simple profile of the standard, which is capable of encoding up to four video objects. At the same time, the compression is selective, maintaining a higher quality for foreground objects and a lower quality for background representation. This property contributes to bandwidth reduction while preserving the essential information of foreground objects. The system performance is demonstrated in experiments that involve objects representing faces and vehicles seen from both static and moving cameras.

  • Smart cameras: 2D affine models for robust person recognition in consumer images

    In this paper we consider how next generation smart cameras will move beyond face detection and tracking to develop more useful analysis tools for determining the expression and emotions of the subjects in images. An initial proof-of-concept using an active appearance model (AAM) to perform face recognition which is robust to pose and illumination variations is presented. Our initial results are very encouraging and suggest that the incorporation of a specialized AAM within next generation digital cameras will enable more accurate management and sorting of personal image collections based on the actual persons in the images.



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