ICCV2017
Venice, Italy, 22/10/2017
Tutorial overview
The drone or drone team, to be managed by the production director and his/her production crew, will have:
01.increased multiple drone decisional autonomy, hence allowing event coverage in the time span of around one hour in an outdoor environment
and
02.improved multiple drone robustness and safety mechanisms (e.g., communication robustness/safety, embedded flight regulation compliance, enhanced crowd avoidance and emergency landing mechanisms), enabling it to carry out its mission against errors or crew inaction and to handle emergencies. Such robustness is particularly important, as the drones will operate close to crowds and/or may face environmental hazards (e.g., wind). Therefore, it must be contextually aware and adaptive, towards maximizing shooting creativity and productivity, while minimizing production costs.
01.drone visual mapping and localization,
02.drone visual analysis for target/obstacle/crowd/POI detection,
03.2D/3D target tracking, d) privacy protection technologies in drones (face de-identification).
The tutorial will offer an overview of all the above plus other related topics:
01.drone odometry and localization (IMU, GPS and other methods),
02.drone formation and flight control,
03.communication issues in drones,
04.security and ethics issues in drones.
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Prof. I. Pitas (pitas@aiia.csd.auth.gr, PhD, IEEE fellow, IEEE Distinguished Lecturer, EURASIP fellow) is Professor of the Department of Informatics, Aristotle University of Thessaloniki). His current interests are in the areas of image/video processing, intelligent digital media, machine learning, human centered interfaces, affective computing, computer vision, 3D imaging and biomedical imaging. He has published over 850 papers, contributed in 44 books in his areas of interest and edited or (co-)authored another 11 books. He has also been member of the program committee of many scientific conferences and workshops. In the past he served as Associate Editor or co-Editor of eight international journals and General or Technical Chair of four international conferences. He participated in 68 R&D projects, primarily funded by the European Union and is/was principal investigator/researcher in 40 such projects. He has 23790+ citations (Google Scholar) to his work and h-index 72+ (Google Scholar). |
Tutorial Schedule
➤↳Drone Vision and Cinematography: An Overview (Prof. I. Pitas) – 8:30-9:00
➤↳Real-time Visual SLAM (Prof. Jose M. M. Montiel) – 9:00-9:45
➤↳Multisensor Fusion for Target Tracking (Prof. J. R. Martínez-de Dios) – 9:45-10:30
Coffee break – 30 min
➤↳ Drone Formation and Flight Control (Prof. Rita Cunha) – 11:00-11:30
➤↳Deep Learning for Drone Vision in Cinematography (Prof. A. Tefas) – 11:30-12:00
➤↳Drone Cinematography (Prof. N. Nikolaidis) – 12:00-12:30
➤↳Privacy Protection Technologies in Drones (Prof. I. Pitas) – 12:30-12:45
Details on the individual sessions
Drone Vision and Cinematography: An Overview (Prof. I. Pitas) - 30 min
The aim of drone cinematography is to develop innovative intelligent single- and multiple-drone platform for media production to cover outdoor events (e.g. sports) that are typically distributed over large expanses, ranging, for example, from a stadium to an entire city.
The drone or drone team, to be managed by the production director and his/her production crew, will have:
a)increased multiple drone decisional autonomy, hence allowing event coverage in the time span of around one hour in an outdoor environment
and
b)improved multiple drone robustness and safety mechanisms (e.g. communication robustness/safety, embedded flight regulation compliance, enhanced crowd avoidance and emergency landing mechanisms), enabling it to carry out its mission against errors or crew inaction and to handle emergencies. Such robustness is particularly important, as the drones will operate close to crowds and/or may face environmental hazards (e.g., wind). Therefore, it must be contextually aware and adaptive, towards maximizing shooting creativity and productivity, while minimizing production costs.
Drone vision plays an important role towards this end, covering the following topics:
a)drone visual mapping and localization,
b)drone visual analysis for target/obstacle/crowd/POI detection,
c)2D/3D target tracking,
d)privacy protection technologies in drones (face de-identification).
The tutorial will offer an overview of all the above plus other related topics:
a)drone odometry and localization (IMU, GPS and other methods),
b)drone formation and flight control,
c)communication issues in drones,
d)security and ethics issues in drones.
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Prof. I. Pitas (for a detailed CV, please take a look above) |
Real-time Visual SLAM (Prof. Jose M. M. Montiel) - 45 min
ORBSLAM, whose source code is available under GPLv3 licence, is a paradigmatic visual SLAM system, it is able to compute, in real-time, the camera trajectory and a sparse 3D reconstruction of the scene in a wide variety of environments. The talk is focused in presenting the synergy among the main building blocks of ORBSLAM: initialization, tracking, mapping, large loop closing and relocalisation from wide baselines.
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Prof. Jose M. M. Montiel is full professor at Universidad de Zaragoza (Spain) where he shares his time between lecturing and research. His publications have appeared in major robotics/vision conferences and journals including IEEE T. Robotics, IEEE T. PAMI, and IEEE ICRA. He has several honours and awards, including the 2016 King-Sun Fu Memorial Best Paper Award, that recognizes the best paper published in 2015 in the IEEE Transactions on Robotics, for co-authoring the article describing the ORB-SLAM system. |
Multisensor fusion for target tracking (Prof. J. R. Martínez-de Dios) - 45 min
Target tracking is a very important functionality in a wide range of applications, including drone filming. There are not ideal sensors for tracking in unstructured environments: each sensor has its pros and cons. This presentation includes some previous decentralised multisensor tracking techniques developed by the presenter and discusses on different multidrone multisensor fusion schemes for efficient and accurate target tracking.
Image coming soon | Prof. J. R. Martínez-de Dios is an Associate Professor (Full Prof. Enabled) with the Robotics, Vision and Control Group at the University of Seville. His main research topics are multi-robot systems, aerial robot perception, cooperative perception and robot-sensor network systems. In these topics he has he coordinated 14 R&D projects and has participated in other 55 projects, including 18 EU-funded in FPs IV, V, VI, VII and H2020 programmes. He has also coordinated and participated in other 20 technology transfer projects to companies such as BOEING, AIRBUS and IBERDROLA, among others. He is author or co-author over 100 publications on multi-robot systems, cooperative perception and sensor fusion. He has as served as co-chair and TPC member in 15 international conferences and is member of the editorial board of 4 indexed international journals. His R&D activities have obtained 5 international awards including one “2010 EURON/EUROP Technology Transfer Award” and one “2017 EU Drone Award”. |
Coffee break
30min to grab something to drink and discuss ideas
Drone formation and flight control (Prof. Rita Cunha) - 45 min
Drones are rapidly evolving to become highly capable sensing platforms that can navigate and track trajectories with great accuracy. Flight feedback control plays a central role in providing position and velocity stabilization, as well as trajectory tracking capabilities, with robustness against measurement errors, poorly modeled dynamics and external disturbances. Feedback control methods range from linear control schemes based on linearized models to more evolved nonlinear control solutions. Under certain conditions, global stability properties can be obtained, while explicitly taking into account disturbances, such as wind and enforcing bounds on the actuation. As flight control of individual drones reaches its maturity, formation control of multiple drones is opening new research avenues. Formation control aims to drive multiple robots to achieve a group motion, while holding a reference geometric shape. This behavior is particularly suited for multiple drone audiovisual shooting scenaria, where multiple drones track a moving target, whose trajectory is not known a-priori for acquiring images from different views. Formation control solutions range from centralized to distributed, position-based to distance-based or bearing-based, considering single integrator to nonlinear vehicle dynamics, or addressing robustness issues, such as sensor or communication-related time delays or packet losses. In this tutorial, we will give an overview of existing solutions for flight control of both individual drones and multiple drones in formation, tested both indoors and outdoors and using different sensor suites.
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Rita Cunha is an Assistant Researcher at the Institute for Systems and Robotics, Lisbon, and an Invited Assistant Professor at the Department of Electrical and Computer Engineering of Instituto Superior Técnico (IST), Universidade de Lisboa, Portugal. She received the Ph.D. degree in Electrical and Computer Engineering from IST in 2007. She is co-author of 22 journal papers and 60 conference papers, with more than 800 citations, h-index 16 (source: Google Scholar). She is the recipient of an Investigator Grant from the Portuguese Science and Technology Foundation and has participated in several national and international research projects (leader in 2 national projects). Her research interests include the general areas of dynamical systems and nonlinear control systems, with application to the development of new methodologies for the control and navigation of autonomous robotic vehicles, in particular, aerial vehicles. |
Deep Learning for Drone Vision in Cinematography (Prof. A. Tefas) - 45 min
Deep Convolutional Neural Networks (CNNs) are among the state-of-the-art techniques for Visual Information Analysis. CNNs can be used to perform several drone visual analysis tasks such as object detection and tracking, face detection and person identification, crowd detection for ensuring the safety of the flight, emergency landing point detection, etc. However, deploying such deep learning models on drones is not a straightforward task, since there are significant memory and model complexity constraints. To overcome these limitations several methodologies have been proposed:
a)training small lightweight CNNs,
b)using knowledge transfer techniques, such as neural-network distillation, layer hints and similarity embeddings, to reduce the size of CNNs
and
c)using neural region proposals for fast object detection and classification (faster R-CNN, YOLO, SSD).
Furthermore, gathering training data suitable for training the deep learning models is also a challenging task. Learning by using dataset augmentation techniques, such as hard negative and positive sample mining, can help to partially overcome this limitation, while allows us to further increase the performance of the trained models. Deep learning techniques can be also used for end-to-end drone control, allowing the deep model to control every aspect of the flight, from the visual information analysis to the drone and camera controls. Using multiple drones (multidrone setup) can increase the flexibility of drone cinematography (several multidrone use-cases are considered). Finally, there is a number of deep learning frameworks that can be used for deploying the aforementioned deep learning techniques on drones (Tensorflow, Caffe, Darknet). The above topics will be covered in the tutorial giving emphasis on specific use cases from drone cinematography.
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Anastasios Tefas received the B.Sc. in informatics in 1997 and the Ph.D. degree in informatics in 2002, both from the Aristotle University of Thessaloniki, Greece. Since 2017 he has been an Associate Professor at the Department of Informatics, Aristotle University of Thessaloniki. From 2008 to 2017, he was a Lecturer, Assistant Professor at the same University. From 2006 to 2008, he was an Assistant Professor at the Department of Information Management, Technological Institute of Kavala. From 2003 to 2004, he was a temporary lecturer in the Department of Informatics, University of Thessaloniki. From 1997 to 2002, he was a researcher and teaching assistant in the Department of Informatics, University of Thessaloniki. Dr. Tefas participated in 12 research projects financed by national and European funds. He has co-authored 74 journal papers, 178 papers in international conferences and contributed 8 chapters to edited books in his area of expertise. Over 3730 citations have been recorded to his publications and his H-index is 32 according to Google scholar. His current research interests include computational intelligence, deep learning, pattern recognition, statistical machine learning, digital signal and image analysis and retrieval and computer vision. |
Drone Cinematography (Prof. N. Nikolaidis) - 45 min
Drones have already made their way into media production, be it cinema movies (Spectre, Captain America: Civil War etc) or TV content (e.g. documentaries and news coverage), and they have done so for a good reason. Indeed, the versatility provided by camera-carrying drones is expected to revolutionize aerial shooting, allowing faster and more flexible camera positioning and movements (including low-altitude ones or shots close to the subject) than those provided by helicopters, while at the same reducing cost and increasing safety and ease of operation. Drones are expected to enable film-makers and TV crews to develop a new cinematographic language, especially in combination with techniques that enable automated and intelligent shooting (a topic that has just started to emerge). This part of the tutorial will review characteristic cases of drone usage in cinematography, provide a taxonomy of existing drone cinematography static & dynamic shot types and shot sequencing, delve into the new horizons that open for the creation of new visual effects and shot types and discuss technical/research issues and challenges as well as issues related to viewer’s experience and perceived quality. It will also review recent approaches for automatic drone cinematography or approaches for the “virtual” planning drone shots. The opportunities and challenges stemming from the use of multiple drones will also be discussed.
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Nikos Nikolaidis received the Diploma of Electrical Engineering and the Ph.D. degree in Electrical Engineering from the Aristotle University of Thessaloniki, Thessaloniki, Greece, in 1991 and 1997, respectively. He is currently Associate Professor at the Department of Informatics, Aristotle University of Thessaloniki. He has co-authored 1 book, 15 book chapters, 57 journal papers and 171 conference papers and co-edited one book and two special issues in journals. Moreover he has co-organized 6 special sessions in international conferences. The number of citations to his work by third authors exceeds 4700 (h-index 28, Source: Google Scholar). He has participated into 24 research projects funded by the EU and national funds. His current areas of interest include multiview video processing/analysis, anthropocentric video analysis (person detection/tracking/recognition, activity recognition), analysis of motion capture data, computer vision, digital image/video processing, computer graphics and visualization. Dr. Nikolaidis is currently serving as associate/area editor for Signal Processing: Image Communication and the EURASIP Journal on Image and Video Processing. He served as Exhibits chair of IEEE ICIP 2001, Technical Program chair of IEEE IVMSP 2013 workshop, and Publicity co-chair of EUSIPCO 2015. He will be Publicity co-chair of ICIP 2018. Dr Nikolaidis is a Senior Member of IEEE and member of the Technical Chamber of Greece. |
Privacy protection technologies in drones (Prof. I. Pitas) - 30 min
Drones raise many important privacy issues: e.g., they should not overfly private space, they should avoid taking unsuspected persons’ closeup views. These ethics restrictions and the relevant legal provisions will be detailed in this lecture. Furthermore, relevant privacy protection tools will be overviewed, namely a) methods for face detection hindering, b) face de-identification, c) tools that detect faces, recognize humans and de-identify the faces of only these persons that are in medium close-ups or close-ups and are not actors.
You can find more information about multiple drone cinematography on the MultiDrone website and in the MultiDrone project presentation.
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