Monthly Archives: September 2016

From a Formalized Parallel Action Language to its Efficient Code Generation

From a Formalized Parallel Action Language to its Efficient Code Generation

  • Ivan Llopard, Christian Fabre, and Albert Cohen. From a Formalized Parallel Action Language to its Efficient Code Generation. ACM Transactions on Embedded Computing Systems, 16(2), 2016. doi:10.1145/2990195
    [BibTeX] [Abstract]

    Modeling languages propose convenient abstractions and transformations to handle the complexity of s embedded systems. Based on the formalism of Hierarchical State Machine, they enable the expression of hierarchical control parallelism. However, they face two importants challenges when it comes to model data-intensive applications: no unified approach that also accounts for data-parallel actions; and no effective code optimization and generation flows. We propose a modeling language extended with parallel action semantics and hierarchical indexed-state machines suitable for computationally intensive applications. Together with its formal semantics, we present an optimizing model compiler aiming for the generation of efficient data-parallel implementations.

    @Article{2016-09-LLOPARD,
    author = {Ivan Llopard and Christian Fabre and Albert Cohen},
    journal = {{ACM Transactions on Embedded Computing Systems}},
    title = {{From a Formalized Parallel Action Language to its Efficient Code Generation}},
    year = {2016},
    date = {2016-09},
    volume = {16},
    number = {2},
    doi = {10.1145/2990195},
    abstract = {Modeling languages propose convenient abstractions and transformations to handle the complexity of s embedded systems. Based on the formalism of Hierarchical State Machine, they enable the expression of hierarchical control parallelism. However, they face two importants challenges when it comes to model data-intensive applications: no unified approach that also accounts for data-parallel actions; and no effective code optimization and generation flows. We propose a modeling language extended with parallel action semantics and hierarchical indexed-state machines suitable for computationally intensive applications. Together with its formal semantics, we present an optimizing model compiler aiming for the generation of efficient data-parallel implementations.}
    }

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Application of Drones and Acoustic and Visual Sensors in Remote Detection and Localization of Objects and Events

Application of Drones and Acoustic and Visual Sensors in Remote Detection and Localization of Objects and Events

  • Piotr Szczuko, Grzegorz Szwoch, Maciej Szczodrak, Jozef Kotus, and Andrzej Czyzewski. Zastosowania dronow i sensorow wizyjnych i akustycznych do zdalnej detekcji i lokalizacji obiektow i zdarzen (Application of Drones and Acoustic and Visual Sensors in Remote Detection and Localization of Objects and Events). In Krajowe Sympozjum Telekomunikacji i Teleinformatyki (KSTiT 2016), Gliwice, Poland, 2016.
    [BibTeX]
    @InProceedings{2016-09-SZCZUKOb,
    author = {Piotr Szczuko and Grzegorz Szwoch and Maciej Szczodrak and Jozef Kotus and Andrzej Czyzewski},
    title = {{Zastosowania dronow i sensorow wizyjnych i akustycznych do zdalnej detekcji i lokalizacji obiektow i zdarzen (Application of Drones and Acoustic and Visual Sensors in Remote Detection and Localization of Objects and Events)}},
    booktitle = {{Krajowe Sympozjum Telekomunikacji i Teleinformatyki (KSTiT 2016)}},
    address = {Gliwice, Poland},
    date = {2016-09-26/2016-09-28},
    year = {2016}
    }

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Distributed Visual Processing

Distributed Visual Processing

  • Andrea Cavallaro. Distributed Visual Processing. Tutorial at IEEE International Conference on Image Processing, 2016.
    [BibTeX] [Abstract]

    This tutorial will cover fundamental aspects, challenges and current solutions in distributed visual processing using networks of self-organising wired and wireless smart cameras, with applications in robotics, security and the Internet-of-Things. The tutorial sets forth the state-of-the-art in state estimation and coalition formation for distributed smart cameras. The tutorial will discuss and demonstrate the latest algorithms with a unified and comprehensive coverage. Using practical examples and illustration as support, the tutorial will introduce the participants in a discussion of the advantages and the limitations of traditional and modern approaches for synchronisation, distributed estimation and distributed processing for decision making and actuation in camera networks. Recent methods will be presented that allow cameras to move and to interact locally forming coalitions adaptively in order to provide coordinated decisions under resource and physical constraints. The tutorial will also discuss how cameras may learn to improve their performance. I will conclude the tutorial by introducing a collection of software resources to help the attendees develop and test distributed signal processing algorithms for wireless smart cameras.

    @Misc{2016-09-CAVALLAROb,
    author = {Andrea Cavallaro},
    title = {{Distributed Visual Processing}},
    howpublished = {Tutorial at IEEE International Conference on Image Processing},
    date = {2016-09-25/2016-09-28},
    year = {2016},
    address = {Phoenix, USA},
    abstract = {This tutorial will cover fundamental aspects, challenges and current solutions in distributed visual processing using networks of self-organising wired and wireless smart cameras, with applications in robotics, security and the Internet-of-Things. The tutorial sets forth the state-of-the-art in state estimation and coalition formation for distributed smart cameras. The tutorial will discuss and demonstrate the latest algorithms with a unified and comprehensive coverage. Using practical examples and illustration as support, the tutorial will introduce the participants in a discussion of the advantages and the limitations of traditional and modern approaches for synchronisation, distributed estimation and distributed processing for decision making and actuation in camera networks. Recent methods will be presented that allow cameras to move and to interact locally forming coalitions adaptively in order to provide coordinated decisions under resource and physical constraints. The tutorial will also discuss how cameras may learn to improve their performance. I will conclude the tutorial by introducing a collection of software resources to help the attendees develop and test distributed signal processing algorithms for wireless smart cameras.}
    }

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Performance evaluation of the parallel object tracking algorithm employing the particle filter

Performance evaluation of the parallel object tracking algorithm employing the particle filter

  • Grzegorz Szwoch. Performance evaluation of the parallel object tracking algorithm employing the particle filter. In 20th IEEE Conference SPA 2016, Signal Processing, Algorithms, Architectures, Arrangements, and Applications, Poznan, Poland, 2016. doi:10.1109/SPA.2016.7763598
    [BibTeX]
    @InProceedings{2016-09-SZWOCH,
    author = {Grzegorz Szwoch},
    title = {{Performance evaluation of the parallel object tracking algorithm employing the particle filter}},
    booktitle = {{20th IEEE Conference SPA 2016, Signal Processing, Algorithms, Architectures, Arrangements, and Applications}},
    date = {2016-09-21/2016-09-23},
    year = {2016},
    doi = {10.1109/SPA.2016.7763598},
    address = {Poznan, Poland}
    }

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Detection of fast incoming objects with a moving camera

Detection of fast incoming objects with a moving camera

  • Fabio Poiesi and Andrea Cavallaro. Detection of fast incoming objects with a moving camera. In British Machine Vision Conference, pages 1-11, York, UK, 2016.
    [BibTeX] [Abstract] [Download PDF]

    Using a monocular camera for early collision detection in cluttered scenes to elude fast incoming objects is a desirable but challenging functionality for mobile robots, such as small drones. We present a novel moving object detection and avoidance algorithm for an uncalibrated camera that uses only the optical flow to predict collisions. First, we estimate the optical flow and compensate the global camera motion. Then we detect incoming objects while removing the noise caused by dynamic textures, nearby terrain and lens distortion by means of an adaptively learnt background-motion model. Next, we estimate the time to contact, namely the expected time for an incoming object to cross the infinite plane defined by the extension of the image plane. Finally, we combine the time to contact and the compensated motion in a Bayesian framework to identify an object-free region the robot can move towards to avoid the collision. We demonstrate and evaluate the proposed algorithm using footage of flying robots that observe fast incoming objects such as birds, balls and other drones.

    @InProceedings{2016-09-POIESI,
    title = {{Detection of fast incoming objects with a moving camera}},
    author = {Fabio Poiesi and Andrea Cavallaro},
    booktitle = {{British Machine Vision Conference}},
    address= {York, UK},
    date = {2016-09-19/2016-09-22},
    year = {2016},
    pages = {1-11},
    url = {http://fabio-poiesi.com/files/papers/conferences/2016_BMVC_DetectionFastIncomingObjects_Poiesi_Cavallaro.pdf},
    abstract = {Using a monocular camera for early collision detection in cluttered scenes to elude fast incoming objects is a desirable but challenging functionality for mobile robots, such as small drones. We present a novel moving object detection and avoidance algorithm for an uncalibrated camera that uses only the optical flow to predict collisions. First, we estimate the optical flow and compensate the global camera motion. Then we detect incoming objects while removing the noise caused by dynamic textures, nearby terrain and lens distortion by means of an adaptively learnt background-motion model. Next, we estimate the time to contact, namely the expected time for an incoming object to cross the infinite plane defined by the extension of the image plane. Finally, we combine the time to contact and the compensated motion in a Bayesian framework to identify an object-free region the robot can move towards to avoid the collision. We demonstrate and evaluate the proposed algorithm using footage of flying robots that observe fast incoming objects such as birds, balls and other drones.}
    }

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Positioning System for Recreated Reality Applications Implemented on a Multi-Processing Embedded System

Positioning System for Recreated Reality Applications Implemented on a Multi-Processing Embedded System

  • Patricia Martinez and Eugenio Villar. Positioning System for Recreated Reality Applications Implemented on a Multi-Processing Embedded System. In I Jornadas de Computacion Empotrada y Reconfigurable (JCER 2016), Salamanca, Spain, 2016.
    [BibTeX]
    @InProceedings{2016-09-MARTINEZ,
    author = {Patricia Martinez and Eugenio Villar},
    title = {{Positioning System for Recreated Reality Applications Implemented on a Multi-Processing Embedded System}},
    booktitle = {{I Jornadas de Computacion Empotrada y Reconfigurable (JCER 2016)}},
    date = {2016-09-14/2016-09-16},
    year = {2016},
    address = {Salamanca, Spain}
    }

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Simple Gait Parameterization and 3D Animation for Anonymous Visual Monitoring Based on Augmented Reality

Simple Gait Parameterization and 3D Animation for Anonymous Visual Monitoring Based on Augmented Reality

  • Piotr Szczuko. Simple Gait Parameterization and 3D Animation for Anonymous Visual Monitoring Based on Augmented Reality. Multimedia Tools and Applications, 75(17):10561-10581, 2016. doi:10.1007/s11042-015-2874-0
    [BibTeX] [Abstract]

    The article presents a method for video anonymization and replacing real human silhouettes with virtual 3D figures rendered on a screen. Video stream is processed to detect and to track objects, whereas anonymization stage employs animating avatars accordingly to behavior of detected persons. Location, movement speed, direction, and person height are taken into account during animation and rendering phases. This approach requires a calibrated camera, and utilizes results of visual object tracking. A procedure for transforming objects visual features and bounding boxes into gait parameters for animated figures is presented. Conclusions and future work perspectives are provided.

    @Article{2016-09-SZCZUKOa,
    author = {Piotr Szczuko},
    title = {{Simple Gait Parameterization and 3D Animation for Anonymous Visual Monitoring Based on Augmented Reality}},
    journal = {{Multimedia Tools and Applications}},
    volume = {75},
    number = {17},
    date = {2016-09-01},
    pages = {10561--10581},
    doi = {10.1007/s11042-015-2874-0},
    issn = {1380-7501},
    publisher = {Springer},
    abstract = {The article presents a method for video anonymization and replacing real human silhouettes with virtual 3D figures rendered on a screen. Video stream is processed to detect and to track objects, whereas anonymization stage employs animating avatars accordingly to behavior of detected persons. Location, movement speed, direction, and person height are taken into account during animation and rendering phases. This approach requires a calibrated camera, and utilizes results of visual object tracking. A procedure for transforming objects visual features and bounding boxes into gait parameters for animated figures is presented. Conclusions and future work perspectives are provided.},
    year = {2016}
    }

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Camera coalitions

Camera coalitions

  • Andrea Cavallaro. Camera coalitions. Invited talk at IEEE-EURASIP Summer School on Signal Processing (S3P-2016), 2016.
    [BibTeX] [Abstract]

    Cameras are everywhere. Miniature high-quality cameras are increasingly worn by people, mounted on dashboards and micro-drones, omnipresent in hallways, streets and stores; and in your smartphone. Countless applications will benefit from the capabilities offered by networks of wireless cameras that can autonomously sense, compute, decide and communicate. These networks are composed of cameras whose algorithms need to adapt in response to unknown or dynamic environments and to changes in the assigned task. In this lecture I will present recent methods for cameras to move and to interact locally based on content and context, and to form coalitions that reach coordinated decisions under resource and physical constraints. I will discuss how cameras self-evaluate their performance and improve the quality of the task they are executing through collaboration, adaptively.

    @Misc{2016-09-CAVALLAROa,
    author = {Andrea Cavallaro},
    title = {{Camera coalitions}},
    howpublished = {Invited talk at IEEE-EURASIP Summer School on Signal Processing (S3P-2016)},
    date = {2016-09-04/2016-09-10},
    year = {2016},
    address = {Trento, IT},
    abstract = {Cameras are everywhere. Miniature high-quality cameras are increasingly worn by people, mounted on dashboards and micro-drones, omnipresent in hallways, streets and stores; and in your smartphone. Countless applications will benefit from the capabilities offered by networks of wireless cameras that can autonomously sense, compute, decide and communicate. These networks are composed of cameras whose algorithms need to adapt in response to unknown or dynamic environments and to changes in the assigned task. In this lecture I will present recent methods for cameras to move and to interact locally based on content and context, and to form coalitions that reach coordinated decisions under resource and physical constraints. I will discuss how cameras self-evaluate their performance and improve the quality of the task they are executing through collaboration, adaptively.}
    }

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Detection of fast incoming objects with a moving camera

Detection of fast incoming objects with a moving camera

  • Fabio Poiesi and Andrea Cavallaro. Detection of fast incoming objects with a moving camera. 2016. Software of Detection of fast incoming objects with a moving camera
    [BibTeX] [Download software]
    @Misc{2016-09-POIESIb,
    author = {Fabio Poiesi and Andrea Cavallaro},
    title = {{Detection of fast incoming objects with a moving camera}},
    note = {Software of Detection of fast incoming objects with a moving camera},
    date = {2016-09-07},
    year = {2016},
    software = {http://www.eecs.qmul.ac.uk/~andrea/avoidance.html}
    }

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Detection of fast incoming objects with a moving camera

Detection of fast incoming objects with a moving camera

  • Fabio Poiesi and Andrea Cavallaro. Detection of fast incoming objects with a moving camera. 2016. Online video, illustration of Detection of fast incoming objects with a moving camera
    [BibTeX] [Watch video]
    @misc{2016-09-POIESIa,
    author = {Fabio Poiesi and Andrea Cavallaro},
    title = {{Detection of fast incoming objects with a moving camera}},
    note = {Online video, illustration of Detection of fast incoming objects with a moving camera},
    date = {2016-09-07},
    year = {2016},
    video = {http://www.eecs.qmul.ac.uk/~andrea/avoidance.html}
    }

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