Aghajan, H

Lam, EYM

Prati, A

Qureshi, F

Tam, VWL

HKU Scholars Hub

Title Guest Editorial Computational and smart camerasAuthor(s) Lam, EYM; Aghajan, H; Prati, A; Qureshi, F; Tam, VWLCitation IEEE Journal on Emerging and Selected Topics in Circuits andSystems, 2013, v. 3, p. 121-124Issued Date 2013URL http://hdl.handle.net/10722/185915Rights Creative Commons: Attribution 3.0 Hong Kong LicenseIEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS, VOL. 3, NO. 2, JUNE 2013 121Guest EditorialComputational and Smart CamerasR ECENT advances in camera hardware, processingcapabilities, storage capacities, and communicationtechnologies suggest an evolution of traditional multi-camerasystems into networks of highly capable computational andsmart cameras. These networks can be seen as visual sensornodes that combine sensing with onboard processing andstorage, and that are able to communicate with other similarsensors in the vicinity. As a future development step, these visualsensor nodes will have the necessary control and coordinationmachinery to set up ad-hoc sensing networks, capable of cap-turing, analyzing, and storing large amounts of image and videodata over extended spaces to support a variety of applications.Opportunities for application development abound in domainsas diverse as smart environments, wearable sensing, security andsurveillance, environmental monitoring and disaster response,traffic management and urban sensing, elderly care and wellnessmonitoring, geo-scale monitoring, etc. These networks mustalso exhibit a great deal of autonomy as the sheer scale of thenetworks and the tremendous amount of data present in themrender human-in-the-loop operation for the most part infeasible.Much work is still needed to realize this vision of ad-hocnetworks of computational and smart cameras capable of pro-viding perceptive coverage of wide areas with little or no humansupervision. Problems arising in the domains of camera hard-ware, computer vision, and sensor networks must be solvedconcomitantly to realize this vision. Computational and smartcamera hardware requires significant sensing and processing ca-pabilities and long lifetimes. Additionally, the camera hardwaremust be able to communicate with other cameras and transmitthe video over the network. Video transmission especially isexpensive in terms of both bandwidth and energy consumption.Furthermore, camera nodes need to work together towards acommon sensing goal. No single camera can observe the entirescene. Collaborative sensing, therefore, is important. The overallcamera network must also be capable of autonomous behavior.It suggests that network level control and coordination strategiesare needed. Lastly, new tools to study and experiment with thesenetworks are required. The goal of this special issue is to bringtogether papers dealing with various aspects of this vision.The paper titled “Automatic Fall Detection and ActivityClassification by a Wearable Embedded Smart Camera” de-velops algorithms that enable a CITRIC-based wearable camerato detect if the subject wearing this camera has fallen, or issitting or standing. It is easy to imagine that such wearable cam-eras can serve an enabling role in a technology solution aimedat independent living for the elderly. In terms of novel cameraarchitectures, the paper titled “Hemispherical Multiple CameraSystem for High Resolution Omni-Directional Light FieldImaging” presents an imaging system capable of recording 30frames per second video at resolutions of 9000 2400 pixels.Digital Object Identifier 10.1109/JETCAS.2013.2256971The system also simultaneously captures the surrounding lightfield in its 360 100 degree field of view.As stated above, the real power of computational and smartcameras lies in the fact that these cameras can be used to con-struct networks of cameras. When considering multiple camerasand camera networks, a natural question is how informationcaptured from multiple cameras can be used for visual analysis.The paper titled “Feature Extraction and Representation forDistributed Multi-View Human Action Recognition” deals withthe problem of activity analysis using multiple viewpoints. Thepaper titled “Dynamic Bayesian Network for UnconstrainedFace Recognition in Surveillance Camera Networks” employsBayesian networks to improve facial recognition rates in itstarget application by exploiting the fact that the person inquestion is being viewed by multiple cameras.Video (data collection) synchronization is a challenge whendealing with multiple cameras. The paper titled “Action-BasedMulti-Camera Synchronization” casts video synchronization asa frame association problem, as opposed to a continuous-timewarping problem. High-level video analysis performed at eachstream is used to solve the frame association problem, in turnsolving the video synchronization problem. Then again, thequestion arises as to how to track objects, individuals, or eventsas they move out of the field of view of one camera and enterthe field of view of another camera This problem is known as“consistent labeling” in computer vision circles and the papertitled “Geometry-Based Object Association and ConsistentLabelling in Multi-Camera Surveillance” assumes a calibratedcamera network and a known scene model to identify the sameperson across multiple cameras. Similarly, the paper titled“Multi-View ML Object Tracking with Online Learning onRiemannian Manifolds by Combining Geometric Constraints”studies the issue of tracking objects in multiple cameras; how-ever, this paper learns appearance models of subjects and usesthem to track them in the presence of short-duration occlusions.Requirements in terms of storage, bandwidth, and energy de-termine the overall “performance” of a camera network. Thepaper titled “Implementation of Wireless Vision Sensor Nodewith a Lightweight Bi-Level Video Coding” develops an energyefficient programmable vision sensor node (VSN). Here, dataintensive vision tasks are performed at the VSN, whereas con-trol centric tasks are performed at the server. This partitioningreduces energy requirements while at the same time keepingthe design complexity low. The paper titled “P-FAD: Real-TimeFace Detection Scheme on Embedded Smart Cameras” presentsa real-time face detection system geared towards embedded de-vices such as mobile phones. The face detection system pre-sented here balances detection speed against accuracy, given theprocessing resources available.Within this theme of power-aware sensing, the paper titled“Multimodal Video Analysis on Self-Powered Resource-Lim-ited Wireless Smart Camera” develops a solar-powered video2156-3357/$31.00 © 2013 IEEE122 IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS, VOL. 3, NO. 2, JUNE 2013sensor node capable of on-board video processing. The visualsensor node is self-reliant in that it does not require an externalpower source for its operation. The video processing capabil-ities of this sensor node are demonstrated by implementingabandoned object detection routines. The paper titled “Charac-terizing a Heterogeneous System for Person Detection in Videousing Histogram of Oriented Gradients: Power Versus SpeedVersus Accuracy” presents a hardware implementation of thepopular histogram of oriented gradients (HOG) detector forperson identification. The work presented herein sheds light onthe power/speed/accuracy tradeoffs when using FPGA, GPU,and CPU units for different stages of the HOG algorithm. HOGdetector is a sophisticated computer vision algorithm and itis expected that the findings presented in this paper can beextrapolated to other vision algorithms.The paper titled “On the Hardware/Software Design andImplementation of a High Definition Multiview Video Surveil-lance System” presents a prototype of a multi-view high-def-inition surveillance system. Here each camera includes fieldprogrammable gate array (FPGA) hardware responsible forsingle-view image analysis, such as background subtrac-tion, tracking, etc. Video processed at each camera is sent to aback-end server equipped with storage and graphical processingunits (GPUs) for higher level multi-view video analysis. Whendiscussing real-time video analysis in camera networks, thepaper titled “Real-Time People Tracking in a Camera Network”uses a GPU-based parallel implementation of particle filtersfor tracking individuals in multiple cameras at real-time rates.The paper titled “An Accurate Algorithm for the Identificationof Fingertips Using an RGB-D Camera” develops a real-timesystem for fingertip tracking using the Microsoft Kinect sensor.For tools and frameworks for studying and prototyping largescale networks of computational and smart cameras, the papertitled “Software Laboratory for Camera Networks Research”presents a distributed virtual vision system that can be usedto simulate camera networks consisting of 100 cameras in3-D environments populated with self-animated virtual humans.Such software tools will become increasingly relevant as webegin to study and model larger networks of computational andsmart cameras.We conclude by expressing our sincere thanks to the IEEEJETCAS Editor-in-Chief, Prof. Massoud Pedram, Deputy-Ed-itor-in-Chief, Prof. Manuel Delgado-Restituto, and the entireteam at the IEEE JETCAS. Of course this issue would not havebeen possible without the hard work of the authors who sub-mitted their papers to this issue. Our sincere thanks also go tothe reviewers, who did a superb job reviewing the submitted pa-pers under very tight deadlines.We hope that you will find this issue illuminating. Happyreading!EDMUND Y. LAM, Guest EditorDepartment of Electrical and Electronic EngineeringThe University of Hong KongPokfulam, Hong KongHAMID AGHAJAN, Guest EditorDepartment of Electrical EngineeringStanford UniversityStanford, CA 94305 USAANDREA PRATI, Guest EditorDepartment of Design and Planning in ComplexEnvironmentsUniversità IUAV di VeneziaVenice, 30135 ItalyFAISAL Z. QURESHI, Guest EditorFaculty of ScienceUniversity of Ontario Institute of TechnologyOshawa, ON L1H 7K4 CanadaVINCENT TAM, Guest EditorDepartment of Electrical and Electronic EngineeringThe University of Hong KongPokfulam, Hong KongEdmund Y. Lam (M’00–SM’05) received the B.S. (with distinction), M.S., and Ph.D. degrees inelectrical engineering from Stanford University, Stanford, CA, USA.At Stanford University, he conducted research for the Programmable Digital Camera project inthe Information Systems Laboratory. He also consulted for industry in the areas of digital camerasystems design and algorithms development. He is now an Associate Professor in Electrical andElectronic Engineering and the founding Director of the Imaging Systems Laboratory at The Uni-versity of Hong Kong, with broad research interests around the theme of computational optics andimaging. During the 2010–2011 academic year, he taught at the Department of Electrical Engi-neering and Computer Science at Massachusetts Institute of Technology as a Visiting AssociateProfessor. He is a Topical Editor of the Journal of the Optical Society of America A, and has guestedited special issues in the Multidimensional Systems and Signal Processing journal, and Journalof Electronic Imaging.Dr. Lam also currently serves as an Associate Editor of the IEEE TRANSACTIONS ONBIOMEDICAL CIRCUITS AND SYSTEMS, and a Guest Editor in the IEEE JOURNAL ON EMERGING AND SELECTED TOPICS INCIRCUITS AND SYSTEMS. In addition, he is active in conference organizations, serving as General Chair of the ACM/IEEEInternational Conference on Distributed Smart Cameras in 2012, Program Chair of OSA Signal Recovery and Synthesis meetingin 2011, and Chair of SPIE conference on Image Processing: Machine Vision Applications in 2012–2013. He is a Fellow of theOptical Society (OSA) and Society of Photo-optical Instrumentation Engineers (SPIE). He was presented an Outstanding YoungResearcher Award, a Best Teacher Award, and an Outstanding Teaching Award (team award) from The University of Hong Kong.IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS, VOL. 3, NO. 2, JUNE 2013 123Hamid Aghajan received the B.S. degree from Sharif University of Technology, Tehran, Iran, in1989, and the M.S. and Ph.D. degrees from Stanford University, Stanford, CA, USA, in 1991 and1995, respectively, all in electrical engineering.He is Director of Stanford’s Ambient Intelligence Research (AIR) Lab, and Wireless SensorNetworks (WSN) Lab, where he has supervised research on multi-camera and sensor networksfor smart environments since 2003. Focus of research in his group is on methods and applica-tions of ambient intelligence with an emphasis on activity recognition with networks of cameras,smart phones, and other sensors. Specific research topics include behavior modeling using long-term sensory data, occupancy modeling of smart buildings for resource efficiency, detection ofanomaly or shift in behavior in elderly care, improving office worker’s well-being through person-alized ergonomic recommendations, analysis of meetings, and avatar-based social interactions. Heis Co-Editor-in-Chief of Journal of Ambient Intelligence and Smart Environments, and has servedas Guest Editor for International Journal of Computer Vision and Computer Vision and Image Un-derstanding.Dr. Aghajan has served as Guest Editor for the IEEE TRANSACTIONS ON MULTIMEDIA and IEEE JOURNAL OF SELECTED TOPICSIN SIGNAL PROCESSING. He was a General Chair of ACM/IEEE ICDSC 2008, AMI 2011, Program Chair of ICDSC 2007, and isa General Chair of ICMI 2012. He has organized workshops, special sessions, or tutorials at ECCV, ACM MM, CVPR, ICCV,ICMI, FG, ECAI, EI, and ICASSP.Andrea Prati (SM’09) received the Laurea (cum laude) in computer engineering in 1998, and thePh.D. degree in information engineering, in 2001.He is currently an Associate Professor at the Department of Design and Planning in ComplexEnvironments of the University IUAV of Venice. He collaborated in several research projects, atregional, national, and international level (such as the project BE SAFE (2006–2010) under theNATO funding scheme Science for Peace). Moreover, he collaborates with several local compa-nies in specific research projects, related mainly to industrial applications of computer vision. Hisresearch interests belong to different themes, from embedded devices for sensor networks in com-puter vision applications, to robotic vision, to multimedia, to performance analysis for multimediacomputers. However, his main research activity is on video-surveillance topics: object tracking indistributed, multi-camera environments; analysis and removal of the shadows; behavior analysisthrough trajectory classification. He recently started to research on mobile vision, i.e., the appli-cation of sophisticated computer vision techniques on mobile phones. He is author of more than130 papers in international journals and conference proceedings, he has been invited speaker, and reviewer for many internationaljournals. He is also a member of the Editorial Board of Journal of Optical Engineering (SPIE) and Journal on Ambient Intelligenceand Smart Environments (IOS Press).Dr. Prati has been the Program Chair of ICIAP 2007. He has been the PC of ACM/IEEE International Conference on DistributedSmart Cameras (ICDSC) in 2011 and 2012, and will be for 2013 edition in Palm Springs, CA, USA. He is also organizing asGeneral Chair the 2014 ICDSC edition in Venice. He is a member of ACM and GIRPR.Faisal Z. Qureshi (M’06) received the M.Sc. degree in electronics (with distinction) from theQuaid-e-Azam University, Islamabad, Pakistan, in 1995, and the M.Sc. and Ph.D. degrees in com-puter science from the University of Toronto, Toronto, ON, Canada, in 2000 and 2007, respectivelyHe is an Assistant Professor of Computer Science and the founding Director of the visual com-puting laboratory at the University of Ontario Institute of Technology (UOIT), Oshawa, Canada.Prior to joining UOIT, he worked as a Software Developer at Autodesk. His research interests in-clude sensor networks, computer vision, and computer graphics. He has also published papers inspace robotics. He has interned at ATR Labs, Kyoto, Japan, AT&T Research Labs, Red Bank, NJ,USA, and MDA Space Missions, Brampton, ON, Canada.Dr. Qureshi is also active in conference organizations, serving as the General co-Chair ofthe Workshop on Camera Networks and Wide-Area Scene Analysis (co-located with CVPR)in 2011–2013, Technical Program Committee Chair for the 2013 ACM/IEEE InternationalConference on Distributed Smart Cameras, and Publicity Chair for the 2013 IEEE InternationalConference on Advanced Video and Signal-Based Surveillance. Additionally, he has served on the program committees of themajor conferences in his fields of expertise. He is a member of the ACM.124 IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS, VOL. 3, NO. 2, JUNE 2013Dr. Vincent Tam is a Senior Teaching Consultant and Honorary Assistant Professor in Departmentof Electrical and Electronic Engineering, The University of Hong Kong. He has been actively in-volved as the principal or co-investigator of various research projects related to human–computerinteraction, uses of smart/video sensors and wireless sensor networks in real-life applications. Heis also serving on the on the Editorial Board of the Journal of Ambient Intelligence and Smart Envi-ronments. He has over 80 refereed publications including five book chapters and 16 journal papers.His research interests include image/video sensors, wireless sensor networks, mobile applications,and e-learning systems.Prof. Tam served as both the Publicity and Workshop Chairs of the 6th ACM/IEEE InternationalConference on Distributed and Smart Cameras (ICDSC 2012). He is serving on the Advisory Boardof the International Association for Smart Learning Environments.

Guest Editorial Computational and smart cameras

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