The Experts below are selected from a list of 249 Experts worldwide ranked by ideXlab platform

Jorg Hahner - One of the best experts on this subject based on the ideXlab platform.

  • application independent in network storage optimization for Distributed Smart Camera systems
    International Conference on Distributed Smart Cameras, 2013
    Co-Authors: Carsten Grenz, Fabian Asam, Jorg Hahner
    Abstract:

    Today's Smart Camera networks are able to execute a wide variety of Distributed algorithms. The usage of high-level algorithms range from image processing, i.e. video analysis and feature extraction, to Distributed observation and control protocols that coordinate the collaboration of Cameras. These algorithms output georeferenced data representing various abstractions of the observed scene as well as the Cameras' states. This data is again used by other algorithms operating on it or visualizing it to the security personnel. We present a novel approach for optimizing the storage of georeferenced sensor data in Smart Camera Networks. Our approach makes use of an access-centric paradigm to adaptively optimize the storage location of data depending on the associated access patterns. Our storage framework offers a generalized interface to be easily usable by any higher-level algorithm. The algorithm's adaptive behaviour leads to a huge reduction of network usage and latency.

  • ICDSC - Application-independent in-network storage optimization for Distributed Smart Camera systems
    2013 Seventh International Conference on Distributed Smart Cameras (ICDSC), 2013
    Co-Authors: Carsten Grenz, Fabian Asam, Jorg Hahner
    Abstract:

    Today's Smart Camera networks are able to execute a wide variety of Distributed algorithms. The usage of high-level algorithms range from image processing, i.e. video analysis and feature extraction, to Distributed observation and control protocols that coordinate the collaboration of Cameras. These algorithms output georeferenced data representing various abstractions of the observed scene as well as the Cameras' states. This data is again used by other algorithms operating on it or visualizing it to the security personnel. We present a novel approach for optimizing the storage of georeferenced sensor data in Smart Camera Networks. Our approach makes use of an access-centric paradigm to adaptively optimize the storage location of data depending on the associated access patterns. Our storage framework offers a generalized interface to be easily usable by any higher-level algorithm. The algorithm's adaptive behaviour leads to a huge reduction of network usage and latency.

  • ICDSC - CamInSens - demonstration of a Distributed Smart Camera system for in-situ threat detection
    2012
    Co-Authors: Carsten Grenz, Colin Kuntzsch, Jorg Hahner, Uwe Jänen, Moritz Menze, David D'angelo, Manfred Bogen, Eduardo Monari
    Abstract:

    The CamInSens system is a next-generation self-organizing video surveillance system that combines research being done in the fields of person-tracking, trajectory analysis, visual analytics, and self-organizing system management algorithms. Its purpose is the online threat detection by analysing anomalies in persons' trajectories. Therefore, robust multi-Camera multi-person tracking is combined with a flexible analysis module, which uses online learning classification algorithms as well as user-generated filters to process the persons' trajectories in the surveillance space.

  • CamInSens - demonstration of a Distributed Smart Camera system for in-situ threat detection
    2012 Sixth International Conference on Distributed Smart Cameras (ICDSC), 2012
    Co-Authors: Carsten Grenz, Colin Kuntzsch, Jorg Hahner, Uwe Jänen, Moritz Menze, David D'angelo, Manfred Bogen, Eduardo Monari
    Abstract:

    The CamInSens system is a next-generation self-organizing video surveillance system that combines research being done in the fields of person-tracking, trajectory analysis, visual analytics, and self-organizing system management algorithms. Its purpose is the online threat detection by analysing anomalies in persons' trajectories. Therefore, robust multi-Camera multi-person tracking is combined with a flexible analysis module, which uses online learning classification algorithms as well as user-generated filters to process the persons' trajectories in the surveillance space.

  • self organising Distributed Smart Camera systems
    Organic Computing, 2011
    Co-Authors: Michael Wittke, Jorg Hahner
    Abstract:

    This article summarises the current status of the Distributed Smart Cameras (DISC) project and gives an outlook to further research.

Christophe Bobda - One of the best experts on this subject based on the ideXlab platform.

  • a Distributed Smart Camera apparatus to enable scene immersion work in progress
    International Conference on Hardware Software Codesign and System Synthesis, 2019
    Co-Authors: Erman Nghonda Tchinda, Danielle Tchuinkou Kwadjo, Christophe Bobda
    Abstract:

    In this paper, we investigate the potential of an immersion technology system implemented on embedded devices. Our system consists of Distributed Smart Cameras with overlapping views, covering numerous viewpoints of a monitored scene so that each Smart Camera knows the position of its neighbors. The system provides an on-demand panoramic field-of-view (FOV) in real-time. To generate the panoramic view, we design and implement an image stitching system using images captured from a subset of adjacent embedded Cameras. We verify the effectiveness of our method in terms of quality of result (QoR) and computation efficiency. Initial results show up 12 FPS with 8MP Cameras.

  • CODES+ISSS - A Distributed Smart Camera apparatus to enable scene immersion: work-in-progress
    Proceedings of the International Conference on Hardware Software Codesign and System Synthesis Companion, 2019
    Co-Authors: Erman Nghonda Tchinda, Danielle Tchuinkou Kwadjo, Christophe Bobda
    Abstract:

    In this paper, we investigate the potential of an immersion technology system implemented on embedded devices. Our system consists of Distributed Smart Cameras with overlapping views, covering numerous viewpoints of a monitored scene so that each Smart Camera knows the position of its neighbors. The system provides an on-demand panoramic field-of-view (FOV) in real-time. To generate the panoramic view, we design and implement an image stitching system using images captured from a subset of adjacent embedded Cameras. We verify the effectiveness of our method in terms of quality of result (QoR) and computation efficiency. Initial results show up 12 FPS with 8MP Cameras.

  • Efficient network clustering for traffic reduction in embedded Smart Camera networks
    Journal of Real-Time Image Processing, 2016
    Co-Authors: Ali Akbar Zarezadeh, Franck Yonga, Christophe Bobda, Michael Mefenza
    Abstract:

    In this work, a clustering approach for bandwidth reduction in Distributed Smart Camera networks is presented. Properties of the environment such as Camera positions and environment pathways, as well as dynamics and features of targets are used to limit the flood of messages in the network. To better understand the correlation between Camera positioning and pathways in the scene on one hand and temporal and spatial properties of targets on the other hand, and to devise a sound messaging infrastructure, a unifying probabilistic modeling for object association across multiple Cameras with disjointed view is used. Communication is efficiently handled using a task-oriented node clustering that partition the network in different groups according to the pathway among Cameras, and the appearance and temporal behavior of targets. We propose a novel asynchronous event exchange strategy to handle sporadic messages generated by non-frequent tasks in a Distributed tracking application. Using a Xilinx-FPGA with embedded Microblaze processor, we could show that, with limited resource and speed, the embedded processor was able to sustain a high communication load, while performing complex image processing computations.

  • ICDSC - Improving video communication in Distributed Smart Camera systems through ROI-based video analysis and compression
    2012
    Co-Authors: Franck Yonga, Christophe Bobda, Ali Zarazadeh
    Abstract:

    In this paper we present an approach aimed at optimizing communications in a Distributed Smart Camera network. The goal is to further reduce the amount of video data to be compressed and transported across the network, thus reducing processing time on Smart Camera nodes and increasing the network bandwidth. We seek to transmit only data of major importance in region of interest of captured frames. These data are then compressed and transmitted across the network. The integration of this data reduction scheme in a fast communication and object exchange mechanism among Smart Camera nodes allows for a real-time processing and coordination of Distributed video applications. Using a hardware/software architecture on FPGA-based Smart Cameras, we further improve the computational and communication performance of the whole system, with time critical parts mapped in hardware while control dominated and high-level modules are maintained in software. Results show that the payload obtained after compressing just the ROI parts of a frame is considerably small to allow every Camera node to be able to process about 30 frames and transmit up to 1500 compressed frames per second.

  • Improving video communication in Distributed Smart Camera systems through ROI-based video analysis and compression
    2012 Sixth International Conference on Distributed Smart Cameras (ICDSC), 2012
    Co-Authors: Franck Yonga, Christophe Bobda, Ali Zarazadeh
    Abstract:

    In this paper we present an approach aimed at optimizing communications in a Distributed Smart Camera network. The goal is to further reduce the amount of video data to be compressed and transported across the network, thus reducing processing time on Smart Camera nodes and increasing the network bandwidth. We seek to transmit only data of major importance in region of interest of captured frames. These data are then compressed and transmitted across the network. The integration of this data reduction scheme in a fast communication and object exchange mechanism among Smart Camera nodes allows for a real-time processing and coordination of Distributed video applications. Using a hardware/software architecture on FPGA-based Smart Cameras, we further improve the computational and communication performance of the whole system, with time critical parts mapped in hardware while control dominated and high-level modules are maintained in software. Results show that the payload obtained after compressing just the ROI parts of a frame is considerably small to allow every Camera node to be able to process about 30 frames and transmit up to 1500 compressed frames per second.

Beate Rinner - One of the best experts on this subject based on the ideXlab platform.

  • static dynamic and adaptive heterogeneity in Distributed Smart Camera networks
    ACM Transactions on Autonomous and Adaptive Systems, 2015
    Co-Authors: Peter R Lewis, Lukas Esterle, Beate Rinner, Arjun Chandra, Jim Torresen
    Abstract:

    We study heterogeneity among nodes in self-organizing Smart Camera networks, which use strategies based on social and economic knowledge to target communication activity efficiently. We compare homogeneous configurations, when Cameras use the same strategy, with heterogeneous configurations, when Cameras use different strategies. Our first contribution is to establish that static heterogeneity leads to new outcomes that are more efficient than those possible with homogeneity. Next, two forms of dynamic heterogeneity are investigated: nonadaptive mixed strategies and adaptive strategies, which learn online. Our second contribution is to show that mixed strategies offer Pareto efficiency consistently comparable with the most efficient static heterogeneous configurations. Since the particular configuration required for high Pareto efficiency in a scenario will not be known in advance, our third contribution is to show how decentralized online learning can lead to more efficient outcomes than the homogeneous case. In some cases, outcomes from online learning were more efficient than all other evaluated configuration types. Our fourth contribution is to show that online learning typically leads to outcomes more evenly spread over the objective space. Our results provide insight into the relationship between static, dynamic, and adaptive heterogeneity, suggesting that all have a key role in achieving efficient self-organization.

  • socio economic vision graph generation and handover in Distributed Smart Camera networks
    ACM Transactions on Sensor Networks, 2014
    Co-Authors: Lukas Esterle, Peter R Lewis, Beate Rinner
    Abstract:

    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.

  • SASO Workshops - CamSim: A Distributed Smart Camera Network Simulator
    2013 IEEE 7th International Conference on Self-Adaptation and Self-Organizing Systems Workshops, 2013
    Co-Authors: Lukas Esterle, Horatio Caine, Peter R Lewis, Beate Rinner
    Abstract:

    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
    Self-Adaptive and Self-Organizing Systems, 2013
    Co-Authors: Peter R Lewis, Lukas Esterle, Arjun Chandra, Beate Rinner
    Abstract:

    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.

  • SASO - Learning to be Different: Heterogeneity and Efficiency in Distributed Smart Camera Networks
    2013 IEEE 7th International Conference on Self-Adaptive and Self-Organizing Systems, 2013
    Co-Authors: Peter R Lewis, Lukas Esterle, Beate Rinner, Arjun Chandra, Xin Yao
    Abstract:

    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.

Lukas Esterle - One of the best experts on this subject based on the ideXlab platform.

  • static dynamic and adaptive heterogeneity in Distributed Smart Camera networks
    ACM Transactions on Autonomous and Adaptive Systems, 2015
    Co-Authors: Peter R Lewis, Lukas Esterle, Beate Rinner, Arjun Chandra, Jim Torresen
    Abstract:

    We study heterogeneity among nodes in self-organizing Smart Camera networks, which use strategies based on social and economic knowledge to target communication activity efficiently. We compare homogeneous configurations, when Cameras use the same strategy, with heterogeneous configurations, when Cameras use different strategies. Our first contribution is to establish that static heterogeneity leads to new outcomes that are more efficient than those possible with homogeneity. Next, two forms of dynamic heterogeneity are investigated: nonadaptive mixed strategies and adaptive strategies, which learn online. Our second contribution is to show that mixed strategies offer Pareto efficiency consistently comparable with the most efficient static heterogeneous configurations. Since the particular configuration required for high Pareto efficiency in a scenario will not be known in advance, our third contribution is to show how decentralized online learning can lead to more efficient outcomes than the homogeneous case. In some cases, outcomes from online learning were more efficient than all other evaluated configuration types. Our fourth contribution is to show that online learning typically leads to outcomes more evenly spread over the objective space. Our results provide insight into the relationship between static, dynamic, and adaptive heterogeneity, suggesting that all have a key role in achieving efficient self-organization.

  • socio economic vision graph generation and handover in Distributed Smart Camera networks
    ACM Transactions on Sensor Networks, 2014
    Co-Authors: Lukas Esterle, Peter R Lewis, Beate Rinner
    Abstract:

    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.

  • SASO Workshops - CamSim: A Distributed Smart Camera Network Simulator
    2013 IEEE 7th International Conference on Self-Adaptation and Self-Organizing Systems Workshops, 2013
    Co-Authors: Lukas Esterle, Horatio Caine, Peter R Lewis, Beate Rinner
    Abstract:

    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.

  • camsim a Distributed Smart Camera network simulator
    Self-Adaptive and Self-Organizing Systems, 2013
    Co-Authors: Lukas Esterle, Horatio Caine, Peter R Lewis, Xi Yao, Ernhard Rinne
    Abstract:

    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
    Self-Adaptive and Self-Organizing Systems, 2013
    Co-Authors: Peter R Lewis, Lukas Esterle, Arjun Chandra, Beate Rinner
    Abstract:

    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.

Andreas Papalambrou - One of the best experts on this subject based on the ideXlab platform.

  • Security and privacy in Distributed Smart Cameras
    Proceedings of the IEEE, 2008
    Co-Authors: Dimitrios N. Serpanos, Andreas Papalambrou
    Abstract:

    Distributed Smart Camera systems are becoming increasingly important in a wide range of applications. As they are often deployed in public space and/or our personal environment, they increasingly access and manipulate sensitive or private information. Their architectures need to address security and privacy issues appropriately, considering them from the inception of the overall system structure. In this paper, we present security and privacy issues of Distributed Smart Camera systems. We describe security requirements, possible attacks, and common risks, analyzing issues at the node and at the network level and presenting available solutions. Although security issues of Distributed Smart Cameras are analogous to networked embedded systems and sensor networks, emphasis is given to special requirements of Smart Camera networks, including privacy and continuous real-time operation.

  • security and privacy in Distributed Smart Cameras to guarantee data authenticity and protect sensitive and private information a wide range of mechanisms and protocols should be included in Smart Camera system design
    Proceedings of the IEEE, 2008
    Co-Authors: Dimitrios N. Serpanos, Andreas Papalambrou
    Abstract:

    Distributed Smart Camera systems are becoming increasingly important in a wide range of applications. As they are often deployed in public space and/or our personal environment, they increasingly access and manipulate sensi- tive or private information. Their architectures need to address security and privacy issues appropriately, considering them from the inception of the overall system structure. In this paper, we present security and privacy issues of Distributed Smart Camera systems. We describe security requirements, possible attacks, and common risks, analyzing issues at the node and at the network level and presenting available solutions. Although security issues of Distributed Smart Cameras are analogous to networked embedded systems and sensor networks, emphasis is given to special requirements of Smart Camera networks, including privacy and continuous real- time operation.