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Huiyu Zhou, BEng, MSc, PhD     

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Job Title: Lecturer (Assistant Professor)

University Email: h.zhou@qub.ac.uk

Office: Room 02.024, ECIT

Phone: +44 28 90971753 (or 90974875)

Research Centre:

The Institute of Electronics, Communications and Information Technology (ECIT)

School of Electronics, Electrical Engineering and Computer Science

Queen's University of Belfast

Belfast, BT3 9DT

United Kingdom


Research interests: Computer vision, human-computer interaction, intelligent systems and signal processing.

ResearcherID: O-2692-2014

ORCID: http://orcid.org/0000-0003-1634-9840


Call for Papers

Biography      Teaching      Research      Selected Publications      Research Team      Invited Presentation


Huiyu Zhou obtained a Bachelor of Engineering degree in Radio Technology from the Huazhong University of Science and Technology of China, and a Master of Science degree in Biomedical Engineering from the University of Dundee of United Kingdom, respectively. He was then awarded a Doctor of Philosophy degree in Computer Vision from the Heriot-Watt University, Edinburgh, United Kingdom, where he worked with Professors Patrick Green and Andy Wallace for his PhD thesis entitled "Efficient ego-motion tracking and obstacle detection using gait analysis".

Dr. Zhou is presently a lecturer at the  Queen's University of Belfast. He has worked in the Guangxi Medical University (China), Elscint Ltd. (Israel), University of Essex (UK), University of London (UK), and Brunel University (UK). He has taken part in the consortiums of a number of research projects in medical image processing, computer vision, intelligent systems and data mining. Currently, he is developing new techniques for event search and retrieval. A recent interview with UK FUTURE TV reveals his research background and interests [VIDEO].

Dr. Zhou has published over 110 peer reviewed papers in the field. He was the recipient of "CVIU 2012 Most Cited Paper Award" and was shortlisted for "MBEC 2006 Nightingale Prize". He also won one of the "Best Paper Awards" in the 1993 Annual Conference of China Association for Medical Devices Industry. He currently serves as the Editor-in-Chief of "Recent Advances in Electrical & Electronic Engineering", and is on the Editorial Boards of several refereed journals. He is a Guest Co-Editor of Pattern Recognition, Neurocomputing, Signal Processing, Signal, Image and Video Processing and Journal of Electrical and Computer Engineering, and a Co-Chair of International Workshop on Sparse Representation for Event Detection in Multimedia (SRED'11). He serves or has served as a technical program committee for 280 conferences in signal and image processing and a reviewer for 85 refereed journals including 19 IEEE Transactions/Journals.

He is an Adviser of International Students in the School. He also is a member of the Invest in People Team (IiP) and the Research Society in the School. 

  • Web and Mobile App Development (CSC3054/7054). Spring, 2015.
  • Web and Mobile App Development (CSC3054/7054). Spring, 2014.
  • Web and Mobile App Development (CSC7054). Spring, 2013.
  • Emerging Human Based Security: Biometrics (CSC7002). Spring, 2010.


Current Project 
  • CSC: Identification of Non-Invasive Tumour In Human Tissue, 2014-2015, Queen's University of Belfast, United Kingdom
    • In this project, we are interested to identify tumour on Haematoxylin & Eosin stained sections, which grow beyond their source into the surrounding normal healthy tissue.
    • Team member: Kun Zhang.
    • Publications: TBA.
  • EPSRC: The Centre for Secure Information Technology (CSIT): Security Convergence For Transport Corridors, 2009-2014, Queen's University of Belfast, United Kingdom
    • In this Grand Challenge, we will design a novel event driven computing system that can detect and process events using a heterogeneous sensor network (e.g. CCTV hardware and other security devices). This system will be applied to two major application areas, i.e. a bus and an airport departure lounge.
    • I am leading WP7 on Event Search and Retrieval. Team members: Sriram Varadarajan, Jeong-Gyoo Kim, and Xin Hong
    • Publications: TCSVT'15PR'15-1CVIU'15PR'14ICDSC'14AVSS'14ECAI'14AVSS'13NEUROCOM'13PR'13BMVC'11
Previous Projects 

  • EPSRC: ISIS - An Integrated Sensor Information System for Crime Prevention, 2009-2010, Queen's University of Belfast, United Kingdom
    • The ISIS project targeted at detecting threats on public transport, informing key decision makers of the threats and managing its own networks. This project used video cameras, audio microphones and RF/microwave sensors to detect threats as they enter buses or trains. In cases where the passenger is unknown, the developed system also used advanced RF scanning techniques to check for external intrusion into the security zone. The novel contribution of this project consists of automatic human profiling and three dimensional object tracking.
    • Publications:  BMVC'11AVSS'10   
  • EU FP6: RUSHES - Retrieval of Multimedia Semantic Units for Enhanced Reusability, 2007-2009, Brunel University, United Kingdom 
    • The aim of the RUSHES project was to design, implement and validate a system for indexing, accessing and delivering raw, unedited audio-visual footage known in the broadcasting industry as "rushes". This project was intended to promote the reuse of such material and especially its content in the production of new multimedia assets by offering semantic media search capabilities. The major contribution includes techniques for automatic content cataloguing and semantic based indexing, knowledge extraction for semantic inference, semantic based content annotation and interactive navigation.
    • Team members: Mohammad Rafiq Swash, Jawid Azizi, Umar A. Sadiq, and Richard M. Jiang
    • Publications: TMM'12TSMC'11NEUROCOM'10RPEE'09PR'08WIAMIS'08-1WIAMIS'08-2CBMI'08   
  • EPSRC: MOTINAS - Multi-model Object Tracking in A Network of Audio-Visual Sensors, 2006-2007, Queen Mary College, University of London, United Kingdom
    • The aim of this project was to develop a novel scheme for multi-model and multi-sensor tracking. The multi-sensor network is composed of STAC sensors (stereo microphones coupled with rectilinear, omni-directional or pan-tilt-zoom cameras). Sound information is used to discriminate ambiguous visual observation as well as to extend the coverage area of the sensors beyond the field of view of the cameras. The major contribution is a robust and adaptive representation of objects based on their acoustical and visual attributes, while the objects are moving across the network of heterogeneous sensors.
    • Publications:  JSTSP'08ICDSC'07    
  • EPSRC: SMART Rehabilitation - Technological Applications for Home Based Stroke Patients, 2004-2006, University of Essex, United Kingdom
    • The aim of this project was to explore how technology could be used to facilitate active in-home rehabilitation for people following stroke. We used orientation sensors that were attached to the wrist and upper limb through clothing reembling sportswere. The major contribution of the project is a sensor fusion strategy accompanied by a decision support interface (TV or computer screen) for stroke rehabilitation.
    • Team member: Yaqin Tao
    • Publications: TSMC'12TIM'10MBEC'08BSPC'08,  MEP'08IJRR'07IJDHD'06BSPC'06MBEC'06HCI'07CWUAAT'06,  ICMA'05          
  • Efficient Ego-Motion Tracking And Obstacle Detection Using Gait Analysis, 2000-2004, Supported by A Multi-Disciplinary Studentship of Heriot-Watt University, United Kingdom
    • The purpose of this study was to design systems that could be used to aid elderly or visually impaired people when they have difficulty in detecting potential tripping hazards. A human gait-based methodology was developed to represent camera motion as a truncated Fourier series. To parameterise this unknown non-linear motion model, we first establish the history of the camera (or human) motion using a classical frame-by-frame analysis, and then apply the iteratively-reweighted least-squares technique for recovery of the motion model. Using the retrieved gait, we undertake an extended "predict-correct" framework (the maximum a posteriori strategy) in order to obtain robust motion estimates. Assuming that the camera position in space has been properly established, we then use this knowledge in combination with the camera motion to construct a safe path ahead of the pedestrian by considering a simple projective invariant analysis.
    • Publications:  NEUROCOM'09SP'09PRL'03ICPR'04
  • Fast Three-Dimensional Imaging Techniques in Echocardiography, 2004-2007, Co-Investigator, Supported by The Guangxi Natural Science Foundation of China
    • The aim of this project was to propose a novel approach for reconstructing the three dimensional structure of a human heart from two dimensional images. Tracking the variations of the structure is used to analyse the dynamics of the heart. Our contribution consists of new techniques for image denoising, structure from motion, image segmentation and object tracking. 
    • Publications:  CMIG'11MTA'10NEUROCOM'08IJPRAI'08,  IJIG'07CMIG'07IJIA'06  
  • Fuzzy C-Means Algorithms For Image Segmentation And Quantisation, 2008-2011, Queen's University of Belfast, United Kingdom
    • This project was in an attempt to seek optimal solutions towards the challenging over- or under-segmentation (or quantisation) problem. We proposed several new schemes for achieving better performance than the other state of the art strategies. The proposed approaches have been evaluated using publicly accessible databases.
    • Publications: FI'12IJHIS'11IJTS'10JSTSP'09TS'09

Selected Publications 

(Complete list of my publications can be found on: University Website) 

Research Team 

  • Kun Zhang, Visiting researcher. Project: Medical image classification, 2014-2015.
  • Andy Park, Part-time PhD student. Project: Human detection from images, 2013-2018.
  • Cristina Surlea, Part-time PhD student. Project: Medical image segmentation, 2013-present.
  • Sriram Varadarajan, PhD student. Project: Dynamic background modelling for foreground detection in surveillance video, 2010-2014, and now works as a researcher in the group. 
  • Dr. Xin Hong, Research Fellow. Project: Event search and retrieval, 2012-present.
  • Carol Campbell, PhD student. Project: Video event recognition using temporal dynamics, 2009-2011, and now works at NYSE, Belfast.
  • Dr. Jiali Shen, Engineer. Project: Multi-camera object tracking, 2010-2012, and now works at NYSE, Belfast.
  • Dr. Jeong-Gyoo Kim, Research Fellow. Project: Shape modelling and human detection, 2011-2013.
 If you work in academia, please take a look at these materials provided by other professionals:  

  • "Evidence reasoning for event inference in smart transport video surveillance", Invited talk, Workshop on Video Soft Service, UCAMI/IWAAL, Belfast, United Kingdom, December, 2014.
  • "Evidence reasoning for event inference in smart transport video surveillance", Special session "Smart cameras for smart environments", Int'l Conf. on Distributed Smart Cameras, Venice, Italy, November, 2014.
  • "'Who are you?': human profiling and behaviour analysis", Ocean University of China, China University of Petroleum (Huadong) and Nanjing University of Aeronautics and Astronautics, China, September, 2014.
  • "Markov Chain Monte Carlo approaches in computer vision", Shanghai University, China, December, 2013.
  • "'Who are you?': human profiling and behaviour analysis", China-UK Symposium on Life System Modeling & Simulation, Colchester, United Kingdom, August, 2013.
  • "Human profiling and event recognition in video surveillance", University of Ulster, United Kingdom, February, 2012.
  • "Multimedia analysis and reuse of raw unedited audiovisual contents", Aston University, United Kingdom, January, 2009.
  • "Human motion tracking and rehabilitation", The Technology Partnership, Melbourn, United Kingdom, February, 2008.
  • "Audiovisual tracking using STAC sensors", University of Portsmouth, United Kingdom, December, 2007.
  • "Smart sensor for human movement tracking", The IEE Seminar on Sensor System for Intelligent Buildings, Birmingham, United Kingdom, November, 2004.
  • "Efficient motion tracking using gait analysis", McGill University, Montreal, Canada, May, 2004.

Last modified by Huiyu Zhou: 22th February, 2015, United Kingdom.

Huiyu Zhou,
21 Sep 2012, 05:27
Huiyu Zhou,
20 Mar 2014, 07:33