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<h1 class="mbr-section-title mbr-white pb-3 mbr-fonts-style display-5">Drone vs. Bird Detection: Deep Learning Algorithms and Results from a Grand Challenge</h1>
<h3 class="mbr-section-subtitle align-left mbr-white mbr-light pb-3 mbr-fonts-style display-7"> A. Coluccia, A. Fascista, A. Schumann, L. Sommer, A. Dimou, D. Zarpalas, M. Méndez, D. de la Iglesia, I. González, J. Mercier, G. Gagné, A. Mitra, S. Rajashekar, S. “Drone vs. Bird Detection: Deep Learning Algorithms and Results from a Grand Challenge”. Sensors 2021, 21(8), 2824, https://www.mdpi.com/1424-8220/21/8/2824</h3>
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<p class="mbr-text mbr-fonts-style display-7"><a href="javascript:OpenModal('BibTeX-dronebird')" class="text-primary">[BibTex]</a> <a href="javascript:OpenModal('Abstract-dronebird')" class="text-primary">[Abstract]</a> <a href="assets/files/sensors-21-02824-v2.pdf" target="_blank" class="text-primary">[PDF]</a> </p>
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Adopting effective techniques to automatically detect and identify small drones is a very compelling need for a number of different stakeholders in both the public and private sectors. This work presents three different original approaches that competed in a grand challenge on the “Drone vs. Bird” detection problem. The goal is to detect one or more drones appearing at some time point in video sequences where birds and other distractor objects may be also present, together with motion in background or foreground. Algorithms should raise an alarm and provide a position estimate only when a drone is present, while not issuing alarms on birds, nor being confused by the rest of the scene. In particular, three original approaches based on different deep learning strategies are proposed and compared on a real-world dataset provided by a consortium of universities and research centers, under the 2020 edition of the Drone vs. Bird Detection Challenge. Results show that there is a range in difficulty among different test sequences, depending on the size and the shape visibility of the drone in the sequence, while sequences recorded by a moving camera and very distant drones are the most challenging ones. The performance comparison reveals that the different approaches perform somewhat complementary, in terms of correct detection rate, false alarm rate, and average precision.</div><div class="modal-footer"><div class="mbr-section-btn"><a href="#" class="no-anim btn btn-secondary display-4" data-dismiss="modal">Close</a></div></div> </div> </div></div><script>document.addEventListener("DOMContentLoaded", function() { $("#Abstract-dronebird").on("hidden.bs.modal", function () { var html = $( "#Abstract-dronebird_body" ).html(); $( "#Abstract-dronebird_body" ).empty(); $( "#Abstract-dronebird_body" ).append(html); })});</script></div>
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<h1 class="mbr-section-title mbr-white pb-3 mbr-fonts-style display-5">Serverless Streaming for Emerging Media: Towards 5G Network-driven Cost Optimization</h1>
<h3 class="mbr-section-subtitle align-left mbr-white mbr-light pb-3 mbr-fonts-style display-7">K. Konstantoudakis, D. Breitgand, A. Doumanoglou, N. Zioulis, A. Weit, K. Christaki, P. Drakoulis, E. Christakis, D. Zarpalas, P. Daras, Multimedia Tools and Applications, 2021. DOI: https://doi.org/10.1007/s11042-020-10219-7</h3>
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title={Serverless streaming for emerging media: towards 5G network-driven cost optimization},
author={Konstantoudakis, Konstantinos and Breitgand, David and Doumanoglou, Alexandros and Zioulis, Nikolaos and Weit, Avi and Christaki, Kyriaki and Drakoulis, Petros and Christakis, Emmanouil and Zarpalas, Dimitrios and Daras, Petros},
journal={Multimedia Tools and Applications},
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publisher={Springer}
}
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Immersive 3D media is an emerging type of media that captures, encodes and reconstructs the 3D appearance of people and objects, with applications in tele-presence, teleconference, entertainment, gaming and other fields. In this paper, we discuss a novel concept of live 3D immersive media streaming in a serverless setting. In particular, we present a novel network-centric adaptive streaming framework which deviates from the traditional client-based adaptive streaming used in 2D video. In our framework the decisions for the production of the transcoding profiles are taken in a centralized manner, by considering consumer metrics vs provisioning costs and inferring the expected consumer quality of experience and behavior based on them. In addition, we demonstrate that a naive application of the serverless paradigm might be sub-optimal under some common immersive 3D media scenarios.</div><div class="modal-footer"><div class="mbr-section-btn"><a href="#" class="no-anim btn btn-secondary display-4" data-dismiss="modal">Close</a></div></div> </div> </div></div><script>document.addEventListener("DOMContentLoaded", function() { $("#Abstract-serverless-stream").on("hidden.bs.modal", function () { var html = $( "#Abstract-serverless-stream_body" ).html(); $( "#Abstract-serverless-stream_body" ).empty(); $( "#Abstract-serverless-stream_body" ).append(html); })});</script></div>
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<h1 class="mbr-section-title mbr-white pb-3 mbr-fonts-style display-5">Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment</h1>
<h3 class="mbr-section-subtitle align-left mbr-white mbr-light pb-3 mbr-fonts-style display-7">A. Chatzitofis, P. Cancian, V. Gkitsas, A. Carlucci, P. Stalidis, G. Albanis, A. Karakottas, T. Semertzidis, P. Daras, C. Giannitto, E. Casiraghi, F. Sposta, G. Vatteroni, A. Ammirabile, L. Lofino, P. Ragucci, M. Laino, A. Voza, A. Desai, M. Cecconi, L. Balzarini, A. Chiti, D. Zarpalas, V. Savevski, International Journal of Environmental Research and Public Health, 18(6), p.2842, 2021, https://www.mdpi.com/1660-4601/18/6/2842/pdf</h3>
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<div><div class="modal fade" id="BibTeX-vol-of-interest" tabindex="-1" role="dialog" aria-labelledby="BibTeX-vol-of-interestLabel" aria-hidden="true"> <div class="modal-dialog modal-lg " style="height:auto" role="document"> <div class="modal-content"><div class="modal-header"> <h5 class="no-anim modal-title" id="BibTeX-vol-of-interestLabel">Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment</h5> <a href="#" class="no-anim close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></a></div><div class="modal-body" id="BibTeX-vol-of-interest_body"><pre class="highlight"><code>@article{chatzitofis2021volume,
title={Volume-of-interest aware deep neural networks for rapid chest CT-based COVID-19 patient risk assessment},
author={Chatzitofis, Anargyros and Cancian, Pierandrea and Gkitsas, Vasileios and Carlucci, Alessandro and Stalidis, Panagiotis and Albanis, Georgios and Karakottas, Antonis and Semertzidis, Theodoros and Daras, Petros and Giannitto, Caterina and others},
journal={International Journal of Environmental Research and Public Health},
volume={18},
number={6},
pages={2842},
year={2021},
publisher={Multidisciplinary Digital Publishing Institute}
}
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Since December 2019, the world has been devastated by the Coronavirus Disease 2019 (COVID-19) pandemic. Emergency Departments have been experiencing situations of urgency where clinical experts, without long experience and mature means in the fight against COVID-19, have to rapidly decide the most proper patient treatment. In this context, we introduce an artificially intelligent tool for effective and efficient Computed Tomography (CT)-based risk assessment to improve treatment and patient care. In this paper, we introduce a data-driven approach built on top of volume-of-interest aware deep neural networks for automatic COVID-19 patient risk assessment (discharged, hospitalized, intensive care unit) based on lung infection quantization through segmentation and, subsequently, CT classification. We tackle the high and varying dimensionality of the CT input by detecting and analyzing only a sub-volume of the CT, the Volume-of-Interest (VoI). Differently from recent strategies that consider infected CT slices without requiring any spatial coherency between them, or use the whole lung volume by applying abrupt and lossy volume down-sampling, we assess only the "most infected volume" composed of slices at its original spatial resolution. To achieve the above, we create, present and publish a new labeled and annotated CT dataset with 626 CT samples from COVID-19 patients. The comparison against such strategies proves the effectiveness of our VoI-based approach. We achieve remarkable performance on patient risk assessment evaluated on balanced data by reaching 88.88%, 89.77%, 94.73% and 88.88% accuracy, sensitivity, specificity and F1-score, respectively.</div><div class="modal-footer"><div class="mbr-section-btn"><a href="#" class="no-anim btn btn-secondary display-4" data-dismiss="modal">Close</a></div></div> </div> </div></div><script>document.addEventListener("DOMContentLoaded", function() { $("#Abstract-vol-of-interest").on("hidden.bs.modal", function () { var html = $( "#Abstract-vol-of-interest_body" ).html(); $( "#Abstract-vol-of-interest_body" ).empty(); $( "#Abstract-vol-of-interest_body" ).append(html); })});</script></div>
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<h1 class="mbr-section-title mbr-white pb-3 mbr-fonts-style display-5">HUMAN4D: A Human-Centric Multimodal
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<h3 class="mbr-section-subtitle align-left mbr-white mbr-light pb-3 mbr-fonts-style display-7">A. Chatzitofis, L. Saroglou, P. Boutis, P. Drakoulis, N. Zioulis, S. Subramanyam, B. Kevelham, C. Charbonnier, P. Cesar, D. Zarpalas, S. Kollias, P. Daras, IEEE Access, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.3026276</h3>
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<div><div class="modal fade" id="BibTeX-HUMAN4D" tabindex="-1" role="dialog" aria-labelledby="BibTeX-HUMAN4DLabel" aria-hidden="true"> <div class="modal-dialog modal-lg " style="height:auto" role="document"> <div class="modal-content"><div class="modal-header"> <h5 class="no-anim modal-title" id="BibTeX-HUMAN4DLabel">HUMAN4D: A Human-Centric Multimodal Dataset for Motions & Immersive Media</h5> <a href="#" class="no-anim close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></a></div><div class="modal-body" id="BibTeX-HUMAN4D_body"><pre class="highlight"><code>@inproceedings{chatzitofis2020human4d,
author= "Chatzitofis, Anargyros and Saroglou, Leonidas and Boutis, Prodromos and Drakoulis, Petros and Zioulis, Nikolaos and Shishir, Subramanyam and Bart, Kevelham and Caecilia, Charbonnier and Pablo, Cesar and Dimitrios, Zarpalas and Stefanos, Kollias and Petros, Daras",
title = "HUMAN4D: A Human-Centric Multimodal
Dataset for Motions & Immersive Media",
booktitle = "IEEE Access, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.3026276",
year = "2020"
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We introduce HUMAN4D, a large and multimodal 4D dataset that contains a variety of human activities simultaneously captured by a professional marker-based MoCap, a volumetric capture and an audio recording system. By capturing 2 female and 2 male professional actors performing various fullbody movements and expressions, HUMAN4D provides a diverse set of motions and poses encountered as part of single- and multi-person daily, physical and social activities (jumping, dancing, etc.), along with multi-RGBD (mRGBD), volumetric and audio data. Despite the existence of multi-view color datasets captured with the use of hardware (HW) synchronization, to the best of our knowledge, HUMAN4D is the first and only public resource that provides volumetric depth maps with high synchronization precision due to the use of intra- and inter-sensor HW-SYNC. Moreover, a spatio-temporally aligned scanned and rigged 3D character complements HUMAN4D to enable joint research on time-varying and highquality dynamic meshes. We provide evaluation baselines by benchmarking HUMAN4D with state-of-theart human pose estimation and 3D compression methods. We apply OpenPose and AlphaPose reaching 70.02% and 82.95% mAPPCKh-0.5 on single- and 68.48% and 73.94% mAPPCKh-0.5 on two-person 2D pose estimation, respectively. In 3D pose, a recent multi-view approach named Learnable Triangulation, achieves 80.26% mAPPCK3D-10cm. For 3D compression, we benchmark Draco, Corto and CWIPC open-source 3D codecs, respecting online encoding and steady bit-rates between 7-155 and 2-90 Mbps for mesh- and pointbased volumetric video, respectively. Qualitative and quantitative visual comparison between mesh-based volumetric data reconstructed in different qualities and captured RGB, showcases the available options with respect to 4D representations. HUMAN4D is introduced to enable joint research on spatio-temporally aligned pose, volumetric, mRGBD and audio data cues. The dataset and its code are available online.
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<h1 class="mbr-section-title mbr-white pb-3 mbr-fonts-style display-5">Service Development Kit for Media-Type Virtualized Network Services in 5G Networks</h1>
<h3 class="mbr-section-subtitle align-left mbr-white mbr-light pb-3 mbr-fonts-style display-7">R. F. Ustok, U. Acar, S. Keskin, D. Breitgand, A. Weit, P. Drakoulis, A. Doumanoglou, N. Zioulis, D. Zarpalas, P. Daras, F. Iadanza, IEEE Communications Magazine, 58(7), pp. 51-57, 2020. DOI: https://doi.org/10.1109/MCOM.001.1900613</h3>
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author={R. F. {Ustok} and U. {Acar} and S. {Keskin} and D. {Breitgand} and A. {Weit} and P. {Drakoulis} and A. {Doumanoglou} and N. {Zioulis} and D. {Zarpalas} and P. {Daras} and F. {Iadanza} and F. {Moscatelli} and G. {Bernini}},
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title={Service Development Kit for Media-Type Virtualized Network Services in 5G Networks},
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Network functions virtualization (NFV) attributes to the substitute of network functions on dedicated appliances such as load balancers and routers with the use of virtualized instances running as software. Any enterprise can easily implement a wide array of network functions using NFV while maximizing efficiencies and introducing new revenue-generating services that are significantly faster and easier than ever before. NFV is a key enabler of the coming 5G infrastructure, supporting the virtualization of various appliances in the network. The scope of this document is to provide a comprehensive overview of the development of NFV-based services, media services in particular, and their specific requirements that are addressed in existing service development kits (SDKs). By supplying an overview of available tools, the development workflow of an example use case on immersive media is demonstrated with an SDK that provides a set of open source tools facilitating the development of media applications.</div><div class="modal-footer"><div class="mbr-section-btn"><a href="#" class="no-anim btn btn-secondary display-4" data-dismiss="modal">Close</a></div></div> </div> </div></div><script>document.addEventListener("DOMContentLoaded", function() { $("#Abstract-ServiceDevelopmentKit").on("hidden.bs.modal", function () { var html = $( "#Abstract-ServiceDevelopmentKit_body" ).html(); $( "#Abstract-ServiceDevelopmentKit_body" ).empty(); $( "#Abstract-ServiceDevelopmentKit_body" ).append(html); })});</script></div>
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<h1 class="mbr-section-title mbr-white pb-3 mbr-fonts-style display-5">DeepMoCap: Deep Optical Motion Capture Using Multiple Depth Sensors and Retro-Reflectors</h1>
<h3 class="mbr-section-subtitle align-left mbr-white mbr-light pb-3 mbr-fonts-style display-7">Anargyros Chatzitofis, Dimitrios Zarpalas, Stefanos Kollias, Petros Daras. MDPI Sensors 2019.</h3>
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<div><div class="modal fade" id="BibTeX-DeepMoCap-Deep-Optical-Motion-Capture-Using-Multiple-Depth-Sensors-and-Retro-Reflectors" tabindex="-1" role="dialog" aria-labelledby="BibTeX-DeepMoCap-Deep-Optical-Motion-Capture-Using-Multiple-Depth-Sensors-and-Retro-ReflectorsLabel" aria-hidden="true"> <div class="modal-dialog modal-lg " style="height:auto" role="document"> <div class="modal-content"><div class="modal-header"> <h5 class="no-anim modal-title" id="BibTeX-DeepMoCap-Deep-Optical-Motion-Capture-Using-Multiple-Depth-Sensors-and-Retro-ReflectorsLabel">DeepMoCap: Deep Optical Motion Capture Using Multiple Depth Sensors and Retro-Reflectors</h5> <a href="#" class="no-anim close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></a></div><div class="modal-body" id="BibTeX-DeepMoCap-Deep-Optical-Motion-Capture-Using-Multiple-Depth-Sensors-and-Retro-Reflectors_body"><pre class="highlight"><code>@article{chatzitofis2019deepmocap,
title={DeepMoCap: Deep optical motion capture using multiple depth sensors and retro-reflectors},
author={Chatzitofis, Anargyros and Zarpalas, Dimitrios and Kollias, Stefanos and Daras, Petros},
journal={Sensors},
volume={19},
number={2},
pages={282},
year={2019},
publisher={Multidisciplinary Digital Publishing Institute}
}
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In this paper, a marker-based, single-person optical motion capture method (DeepMoCap) is proposed using multiple spatio-temporally aligned infrared-depth sensors and retro-reflective straps and patches (reflectors). DeepMoCap explores motion capture by automatically localizing and labeling reflectors on depth images and, subsequently, on 3D space. Introducing a non-parametric representation to encode the temporal correlation among pairs of colorized depthmaps and 3D optical flow frames, a multi-stage Fully Convolutional Network (FCN) architecture is proposed to jointly learn reflector locations and their temporal dependency among sequential frames. The extracted reflector 2D locations are spatially mapped in 3D space, resulting in robust 3D optical data extraction. The subject’s motion is efficiently captured by applying a template-based fitting technique on the extracted optical data. Two datasets have been created and made publicly available for evaluation purposes; one comprising multi-view depth and 3D optical flow annotated images (DMC2.5D), and a second, consisting of spatio-temporally aligned multi-view depth images along with skeleton, inertial and ground truth MoCap data (DMC3D). The FCN model outperforms its competitors on the DMC2.5D dataset using 2D Percentage of Correct Keypoints (PCK) metric, while the motion capture outcome is evaluated against RGB-D and inertial data fusion approaches on DMC3D, outperforming the next best method by 4.5% in total 3D PCK accuracy.</div><div class="modal-footer"><div class="mbr-section-btn"><a href="#" class="no-anim btn btn-secondary display-4" data-dismiss="modal">Close</a></div></div> </div> </div></div><script>document.addEventListener("DOMContentLoaded", function() { $("#Abstract-DeepMoCap-Deep-Optical-Motion-Capture-Using-Multiple-Depth-Sensors-and-Retro-Reflectors").on("hidden.bs.modal", function () { var html = $( "#Abstract-DeepMoCap-Deep-Optical-Motion-Capture-Using-Multiple-Depth-Sensors-and-Retro-Reflectors_body" ).html(); $( "#Abstract-DeepMoCap-Deep-Optical-Motion-Capture-Using-Multiple-Depth-Sensors-and-Retro-Reflectors_body" ).empty(); $( "#Abstract-DeepMoCap-Deep-Optical-Motion-Capture-Using-Multiple-Depth-Sensors-and-Retro-Reflectors_body" ).append(html); })});</script></div>
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