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  • 1.
    Eriksson, Emil
    et al.
    KTH, School of Electrical Engineering (EES).
    DánDán, György
    KTH, School of Electrical Engineering (EES).
    Fodor, Viktoria
    KTH, School of Electrical Engineering (EES).
    Coordinating Distributed Algorithms for Feature Extraction Offloading in Multi-Camera Visual Sensor Networks2018In: IEEE transactions on circuits and systems for video technology (Print), ISSN 1051-8215, E-ISSN 1558-2205, Vol. 28, no 11, p. 3288-3299Article in journal (Refereed)
    Abstract [en]

    Real-time visual analysis tasks, like tracking and recognition, require swift execution of computationally intensive algorithms. Visual sensor networks could be enabled to perform such tasks by allowing the camera nodes to offload their computational load to nearby processing nodes. In this paper, we address the problem of minimizing the completion time of multiple camera sensors that share the transmission and the processing resources of multiple processing nodes for computation offloading. We show that the problem is NP-hard, and propose a combination of central coordination and distributed optimization with limited signaling among the camera sensors as a solution. We analyze the existence of equilibrium allocations for the distributed algorithms, evaluate the effect of the network topology and of the video characteristics on the algorithms' performance, and assess the benefits of central coordination. Our results demonstrate that with sufficient information available, distributed optimization can provide low completion times, moreover predictable and stable performance can be achieved with additional, sparse central coordination.

  • 2. Flierl, Markus
    et al.
    Girod, Bernd
    Stanford University.
    Generalized B pictures and the draft H.264/AVC video-compression standard2003In: IEEE transactions on circuits and systems for video technology (Print), ISSN 1051-8215, E-ISSN 1558-2205Article in journal (Refereed)
  • 3. Flierl, Markus
    et al.
    Mavlankar, Aditya
    Stanford University.
    Girod, Bernd
    Stanford University.
    Motion and Disparity Compensated Coding for Multiview Video2007In: IEEE transactions on circuits and systems for video technology (Print), ISSN 1051-8215, E-ISSN 1558-2205Article in journal (Refereed)
  • 4. Flierl, Markus
    et al.
    Wiegand, Thomas
    TU Berlin.
    Girod, Bernd
    Stanford University.
    Rate-constrained multihypothesis prediction for motion-compensated video compression2002In: IEEE transactions on circuits and systems for video technology (Print), ISSN 1051-8215, E-ISSN 1558-2205Article in journal (Refereed)
  • 5.
    Kozica, Ermin
    et al.
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Ramchandran, Kannan
    University of California, Berkeley.
    Kleijn, W. Bastiaan
    KTH, School of Electrical Engineering (EES), Sound and Image Processing.
    Power-Constrained Low-Latency Video Encoding using Feedback2010In: IEEE transactions on circuits and systems for video technology (Print), ISSN 1051-8215, E-ISSN 1558-2205Article in journal (Other academic)
  • 6.
    Pauwels, Karl
    et al.
    KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Rubio, Leonardo
    Ros, Eduardo
    Real-time Pose Detection and Tracking of Hundreds of Objects2015In: IEEE transactions on circuits and systems for video technology (Print), ISSN 1051-8215, E-ISSN 1558-2205Article in journal (Refereed)
    Abstract [en]

    We propose a novel model-based method for tracking the six-degrees-of-freedom (6DOF) pose of a very large number of rigid objects in real-time. By combining dense motion and depth cues with sparse keypoint correspondences, and by feeding back information from the modeled scene to the cue extraction process, the method is both highly accurate and robust to noise and occlusions. A tight integration of the graphical and computational capability of graphics processing units (GPUs) allows the method to simultaneously track hundreds of objects in real-time. We achieve pose updates at framerates around 40 Hz when using 500,000 data samples to track 150 objects using images of resolution 640x480. We introduce a synthetic benchmark dataset with varying objects, background motion, noise and occlusions that enables the evaluation of stereo-vision-based pose estimators in complex scenarios. Using this dataset and a novel evaluation methodology, we show that the proposed method greatly outperforms state-of-the-art methods. Finally, we demonstrate excellent performance on challenging real-world sequences involving multiple objects being manipulated.

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