Homepage of Tilo Burghardt

TEACHING (Current term 2018/19)


COMSM0018: Applied Deep Learning (lecturer) + Symposium
COMSM0020: Image Proc & Comp Vision [Robotics] (lecturer)
COMS30121: Image Proc & Comp Vision [CS] (lecturer)
COMS30400: Group Project (unit director)
COMS20001: Concurrent Computing (lecturer)
COMS30500: BSc Final Year Project (Supervisor, Reviewer)
COMSM0111: MEng Final Year Project (Supervisor, Reviewer)
COMSM2202: Research Skills (Supervisor, Reviewer)
COMSM3100: MSc Advanced Project (Supervisor, Reviewer)
COMSM3201: MSc Project Computer Science (Supervisor)

CONTACT


MVB3.42, Woodland Rd, BS8 1UB
+44 (0) 117 954 5298 | tilo (at) cs.bris.ac.uk

ADMIN + EDITORIAL ROLES



(Links: SAFE, BB, TT, Pu, fEC, G, C, O, GM)



SUMMARY

Dr Tilo Burghardt's research focuses on applied computer vision and animal biometrics. His interests include the robust visual detection and identification in unconstrained environments. He contributed to establishing animal biometrics as an emerging cross-discipline routed in pattern recognition and computer vision. Tilo's enthusiasm for computer science and vision is reflected in his dedication to teaching the subject. In 2018 he received the University of Bristol 'Award for Education' for his educational contributions to the Engineering Faculty.

Tilo graduated with Distinction in Media Computing (Bakk. Medien-Inf.) at Dresden University of Technology (Germany). Subsequently, he received an MSc in Advanced Computing and PhD in Computer Vision from the University of Bristol (UK). After initial post-doctoral research at the School of Physics, he was awarded a Fellowship of the Research Councils UK and then became employed as a Lecturer and then Senior Lecturer at the Visual Information Laboratory and the Intelligent Systems Laboratory at the University of Bristol.

Tilo is Associate Editor of IET Computer Vision. He is a member of the British Machine Vision Association (BMVA) and the German Academic Foundation (Studienstiftung des Deutschen Volkes). He is a Fellow of the Higher Education Academy (HEA). Tilo has been a local organizer for the 23rd European Conference of Machine Learning (ECML-PKDD). He has been a chair of the 24th British Machine Vision Conference.



RESEARCH STUDENTS:


Current Students:

  • William Andrew (PhD student, Co-supervisor C Greatwood)
  • Xinyu Yang (PhD student, Co-supervisor M Mirmehdi)
  • Axel Montout (PhD student, Co-supervisor A Dowsey)

Current Research Assistants:

  • Dr Alessandro Masullo (with D Damen and M Mirmehdi)
  • Dr Toby Perrett (with D Damen and M Mirmehdi)

Graduated Alumni:

  • Dr Ben Hughes (PhD 2016)
  • Dr Roz Sandwell (PhD 2015)
  • Luke Palmer (MSc by Research 2014)


WHAT IS ANIMAL BIOMETRICS?


'Animal biometrics is an emerging field that develops quantified approaches for representing and detecting the phenotypic appearance of species, individuals, behaviors, and morphological traits. It operates at the intersection between pattern recognition, ecology, and information sciences, producing computerized systems for phenotypic measurement and interpretation. Animal biometrics can benefit a wide range of disciplines, including biogeography, population ecology, and behavioral research. (...) However, to advance animal biometrics will require integration of methodologies among the scientific disciplines involved. Such efforts will be worthwhile because the great potential of this approach rests with the formal abstraction of phenomics, to create tractable interfaces between different organizational levels of life.'
[cited from TREE2013]


RECENT ANIMAL BIOMETRICS RESEARCH



PROJECT: Active Deep Vision for Friesian Cattle Monitoring
with W Andrew, C Greatwood, Farscope CDT, VILab and BVS

PROJECT: Automating Fin Identification of Great White Sharks
with B Hughes and The SaveOurSeas Foundation

ECOLOGICAL VISION: Applications and Foundations
with Local and International Partners


WHAT IS THE SPHERE PROJECT?


'SPHERE is a community of nearly 100 researchers [...] using a unique platform of sensors to quantify health-related behaviours over long periods to diagnose and help manage health and wellbeing conditions. The technology will aid early diagnosis, lifestyle change and the ability of patients to live at home while maintaining their privacy and independence. [...] SPHERE has been named a World Technology Award Winner in the Organisation: Health and Medicine Catagory by the World Technology Network ('The WTN') - a global community comprised of the most innovative people and organisations at the forefront of science, technology and related fields.'
[cited from SPHERE WEBSITE]


RECENT DIGITAL HEALTH RESEARCH



PROJECT: SPHERE and Indoor Calorie Estimation from Vision
with A Massulo, L Tao, M Mirmehdi, D Damen, S Hannuna and THE SPHERE Team


PREVIOUS TEACHING


COMSM0018: Applied Deep Learning (lecturer)
COMSM0020: Image Proc & Comp Vision [Robotics] (lecturer)
COMS30121: Image Proc & Comp Vision [CS] (lecturer)
COMS30400: Group Project (unit director)
COMS20001: Concurrent Computing (unit director)
COMS10001: Programming & Algorithms 2 (unit director)
COMS10004: Programming & Algorithms 2A (unit director)
COMS10009: Object-Oriented Programming (unit director)
COMS12800: Introduction to C++ (unit director)
COMS20600: Concurrency (unit director)
COMS22201: Language Engineering (lecturer)
COMS30500: BSc Final Year Project (Supervisor, Reviewer)
COMSM0111: MEng Final Year Project (Supervisor, Reviewer)
COMSM2202: Research Skills (Supervisor, Reviewer)
COMSM3100: MSc Advanced Project (Supervisor, Reviewer)
COMSM3201: MSc Project Computer Science (Supervisor) EMATM0009: Complexity in Engineering and Science (lecturer)


CURRENT STUDENT PROJECTS


    Deep Open Set Identification of Individual Manta Rays (MEng, B Fossett)
    Auto-marking of Handwritten Linear Algebra Papers (MSc, N Drake)
    Forest Canape Segmentation and Classification using RNNs (MSc, I Myttas)
    Cost Functions and Multi-Sample Learning for Deep Animal Identification (MSc, X Zhang)
    Supervised vs Reward-based Learning for Search and Rescue (BSc, JQ Ovalle)
    Shark Fin Segmentation and ID via Deconvolutional Networks (BSc, L Zhang)
    Black and White Sport Footage Recolouration with Cycle GANs (BSc, J Atton)


RECENT PREVIOUS STUDENT PROJECTS


    Deep Learning for the Identification of Individual Manta Rays
    Deep Learning for Classifying Marine Calcareous Microfossils
    Individual Elephant Identification using DNN Biometrics
    Style Transfer via Cycle Loss using Deep Neural Networks
    Whiteboard2Website: Interactive Digital Visual Content Generation Tool
    Great Ape Detection by Motion Signature using CNNs
    CNN-based 3D Key Reconstruction from Photography
    From Spider Web Photos to Spider Web Simulations
    Ecological Prediction using Deconvolutional and Convolutional Neural Nets
    Iris Recognition using Low-cost Portable Devices
    Elephant Facial Biometrics using Convolutional Neural Networks
    Automatic Visual Detection of Speech and Pausing in Testimony Videos
    Classifying Marine Calcareous Microfossils
    Evaluating CNN Models for Animal Detection in Mobile Vision Applications
    Detecting Gorillas in Natural Images using CNNs
    LoD for Elephant-part Recognition using Hierarchical DPBMs
    CatIdentifier: Which cat is in the Photo?
    Music Score Interpretation from Sketches using a Native Mobile Platform
    Mammography Interpretation Tool using Computer Vision
    Insect Species Recognition via Combined Local and Global Features
    Enhancing AR Museum Guides Using Markerless Tracking and 3D Model Generation in a Web-Browser
    Visual Identification and Comparison of Bristol graffiti
    Object Specific Haar-like Features for Fast and Accurate Shark Fin Detection
    Salient Object Detection for Navigation of Mars-like Environments
LATEST PUBLICATIONS
T Celik, B Hughes, T Burghardt. Towards End-to-End DNN-based Identification of Individual Manta Rays from Sparse Imagery 23rd Iberoamerican Congress on Pattern Recognition (CIARP), November 2018. (DOI:10.1007/978-3-030-13469-3), (Dataset Manta2018)
W Andrew, C Greatwood, T Burghardt. Deep Learning for Exploration and Recovery of Uncharted and Dynamic Targets from UAV-like Vision. 31st IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1124-1131, October 2018. (DOI:10.1109/IROS.2018.8593751), (IEEE Version), (Dataset GTRF2018), (Video Summary)
R Smith, T Burghardt. DeepKey: Towards End-to-End Physical Key Replication From a Single Photograph. 40th German Conference on Pattern Recognition (GCPR), October 2018. (DOI:10.1007/978-3-030-12939-2), (Arxiv PDF)
A Massulo, T Burghardt, D Damen, S Hannuna, V Ponce-Lopez, M Mirmehdi. CaloriNet: From Silhouettes to Calorie Estimation in Private Environments. 29th British Machine Vision Conference (BMVC), September 2018. (BMVA Version), (Arxiv PDF), (Dataset SPHERE-Calorie)
V Ponce-Lopez, T Burghardt, S Hannuna, D Damen, A Masullo, M Mirmehdi. Semantically Selective Augmentation for Deep Compact Person Re-Identification. Person in Context (PIC) Workshop at European Conference of Computer Vision (ECCV), September 2018. (Arxiv PDF)
A Elsts, T Burghardt, D Byrne, D Damen, X Fafoutis, S Hannuna, V Ponce-Lopez, A Masullo, M Mirmehdi, G Oikonomou, R Piechocki, E Tonkin, A Vafeas, P Woznowski, I Craddock. A Guide to the SPHERE 100 Homes Study Dataset. Technical Report, May 2018. (Arxiv PDF), (Sensor Overview)
B Wang, L Tao, T Burghardt, M Mirmehdi. Calorific Expenditure Estimation using Deep Convolutional Network Features. Computer Vision for Active and Assisted Living Workshop (CV-AAL) at IEEE Winter Conference on Applications of Computer Vision (WACV), March 2018. (DOI:10.1109/WACVW.2018.00014), (Dataset SPHERE-Calorie)
T Burghardt, RB Fisher, S Ravela (editors). Computer Vision for Animal Biometrics. IET Computer Vision - Special Issue, Vol 12, Issue 2, pp.119-120, March 2018. (DOI:10.1049/iet-cvi.2018.0019), (IET Online Version), (Editorial PDF)
L Tao, T Burghardt, M Mirmehdi, D Damen, A Cooper, M Camplani, S Hannuna, A Paiment, I Craddock. Energy Expenditure Estimation using Visual and Inertial Sensors. IET Computer Vision, Special Section: Computer Vision in Healthcare and Assisted Living, Vol 12, Issue 1, pp. 36-47, February 2018. (DOI:10.1049/iet-cvi.2017.0112), (Dataset SPHERE-Calorie)
CA Brust, T Burghardt, M Groenenberg, C Kaeding, HS Kuehl, M Manguette, J Denzler. Towards Automated Visual Monitoring of Individual Gorillas in the Wild. Visual Wildlife Monitoring (VWM) Workshop at IEEE International Conference of Computer Vision (ICCVW), pp. 2820-2830, October 2017. (DOI:10.1109/ICCVW.2017.333), (Dataset Gorilla2017), (CVF Version)
W Andrew, C Greatwood, T Burghardt. Visual Localisation and Individual Identification of Holstein Friesian Cattle via Deep Learning. Visual Wildlife Monitoring (VWM) Workshop at IEEE International Conference of Computer Vision (ICCVW), pp. 2850-2859, October 2017. (DOI:10.1109/ICCVW.2017.336), (Dataset FriesianCattle2017), (Dataset AerialCattle2017), (CVF Version)
G Camps-Valls, T Hickler, B Koenig-Ries (editors). Computer Science Meets Ecology (Dagstuhl Seminar 17091). In: Dagstuhl Reports, Vol 7, No 2, pp. 109-134, Leibniz-Zentrum fuer Informatik, September 2017. (DOI:10.4230/DagRep.7.2.109)
M Camplani, A Paiment, M Mirmehdi, D Damen, S Hannuna, T Burghardt, L Tao. Multiple Human Tracking in RGB-D Data: A Survey. IET Computer Vision. Vol 11(4):265-285, ISSN: 1751-9632. June 2017. (DOI:10.1049/iet-cvi.2016.0178)
B Hughes, T Burghardt. Automated Visual Fin Identification of Individual Great White Sharks. International Journal of Computer Vision (IJCV), Vol 122, No 3, pp. 542-557, May 2017. (DOI:10.1007/s11263-016-0961-y), (Dataset FinsScholl2456)
AS Crunchant, M Egerer, A Loos, T Burghardt, K Zuberbuehler, K Corogenes, V Leinert, L Kulik, HS Kuehl. Automated Face Detection for Occurrence and Occupancy Estimation in Chimpanzees. American Journal of Primatology. Vol 79, Issue 3, ISSN: 1098-2345. March 2017. (DOI 10.1002/ajp.22627)
PR Woznowski, A Burrows, T Diethe, X Fafoutis, J Hall, S Hannuna, M Camplani, N Twomey, M Kozlowski, B Tan, N Zhu, A Elsts, A Vafeas, A Paiement, L Tao, M Mirmehdi, T Burghardt, D Damen, P Flach, R Piechocki, I Craddock, G Oikonomou. SPHERE: A Sensor Platform for Healthcare in a Residential Environment. Designing, Developing, and Facilitating Smart Cities: Urban Design to IoT Solutions. (V Angelakis, E Tragos, HC Poehls, A Kapovits, A Bassi (eds.)), Springer, pp. 315-333, January 2017. (DOI:10.1007/978-3-319-44924-1_14)
L Tao, T Burghardt, M Mirmehdi, D Damen, A Cooper, M Camplani, S Hannuna, A Paiment, I Craddock. Calorie Counter: RGB-Depth Visual Estimation of Energy Expenditure at Home. Lecture Notes in Computer Science (LNCS), Vol 10116, pp. 239-251, November 2016. (DOI:10.1007/978-3-319-54407-6), (Dataset SPHERE-Calorie)
S Hannuna, M Camplani, J Hall, M Mirmehdi, D Damen, T Burghardt, A Paiment, L Tao. DS-KCF: A real-time tracker for RGB-D data. Journal of Real-Time Image Processing. pp.1-20, November 2016. (DOI 10.1007/s11554-016-0654-3), (Rotational Dataset), (Code Download)
J Hall, M Camplani, S Hannuna, M Mirmehdi, L Tao, D Damen, T Burghardt, A Paiment. Designing a Video Monitoring System for AAL applications: The SPHERE Case Study. IET International Conference on Technologies for Active and Assisted Living (TechAAL). October 2016. (DOI:10.1049/ic.2016.0061)
L Tao, T Burghardt, M Mirmehdi, D Damen, A Cooper, M Camplani, S Hannuna, A Paiment, I Craddock. Real-time Estimation of Physical Activity Intensity for Daily Living. IET International Conference on Technologies for Active and Assisted Living (TechAAL). October 2016. (DOI:10.1049/ic.2016.0060)
W Andrew, S Hannuna, N Campbell, T Burghardt. Automatic Individual Holstein Friesian Cattle Identification via Selective Local Coat Pattern Matching in RGB-D Imagery. IEEE International Conference on Image Processing (ICIP), pp. 484-488, ISBN: 978-1-4673-9961-6, September 2016. (DOI:10.1109/ICIP.2016.7532404), (Dataset FriesianCattle2015)
L Tao, A Paiment, D Damen, M Mirmehdi, S Hannuna, M Camplani, T Burghardt, I Craddock. A Comparative Study of Pose Representation and Dynamics Modelling for Online Motion Quality Assessment. Computer Vision and Image Understanding, 148:136-152, Elsevier, May 2016. (DOI:10.1016/j.cviu.2015.11.016)
L Tao, T Burghardt, S Hannuna, M Camplani, A Paiement, D Damen, M Mirmehdi, I Craddock. A Comparative Home Activity Monitoring Study using Visual and Inertial Sensors. IEEE 17th International Conference on eHealth Networking, Applications and Services. pp.644-647, October 2015. (DOI:10.1109/HealthCom.2015.7454583), (Dataset SPHERE_H130)
D Gibson, T Burghardt, N Campbell, N Canagarajah. Towards Automating Visual In-Situ Monitoring of Crops Health. IEEE International Conference on Image Processing (ICIP), pp. 3906 - 3910, September 2015. (DOI:10.1109/ICIP.2015.7351537)
B Hughes, T Burghardt. Automated Identification of Individual Great White Sharks from Unrestricted Fin Imagery. 26th British Machine Vision Conference (BMVC), pp. 92.1-92.14, ISBN 1-901725-53-7, BMVA Press, September 2015. (DOI:10.5244/C.29.92), (Dataset FinsScholl2456)
M Camplani, S Hannuna, D Damen, M Mirmehdi, A Paiement, T Burghardt, L Tao. Robust Real-time RGB-D Tracking with Depth Scaling Kernelised Correlation Filters. 26th British Machine Vision Conference (BMVC), pp. 145.1-145.11, ISBN 1-901725-53-7, BMVA Press, September 2015. (DOI:10.5244/C.29.145), (Code Download)
B Hughes, T Burghardt. Affinity Matting for Pixel-accurate Fin Shape Recovery from Great White Shark Imagery. Machine Vision of Animals and their Behaviour (MVAB), Workshop at BMVC, pages 8.1-8.8. BMVA Press, September 2015. (DOI:10.5244/CW.29.MVAB.8), (Dataset FinsScholl2456)
T Burghardt, D Damen, W Mayol-Cuevas, M Mirmehdi (editors). Correspondence, Matching and Recognition. International Journal of Computer Vision (IJCV), Volume 113, Issue 3:161-162, ISSN 0920-5691, Springer, June 2015. (DOI:10.1007/s11263-015-0827-8)
P Woznowski, F Fafoutis, T Song, S Hannuna, M Camplani, L Tao, A Paiement, E Mellios, M Haghighi, N Zhu, G Hilton, D Damen, T Burghardt, M Mirmehdi, R Piechocki, D Kaleshi, I Craddock. A Multi-modal Sensor Infrastructure for Healthcare in Residential Environment. IEEE ICC Workshop on ICT-enabled Services and Technologies for eHealth and Ambient Assisted Living, 271-277, (DOI:10.1109/ICCW.2015.7247190), June 2015.
L Palmer, T Burghardt. Contextual Saliency for Nonrigid Landmark Registration and Recognition of Natural Patterns. International Conference on Computer Vision Theory and Applications (VISAPP), 403-410, ISBN: 978-989-758-089-5, March 2015. (DOI:10.5220/0005268604030410)
HS Kuehl, T Burghardt. Fractal Representation and Recognition for Animal Biometrics: A Reply to Jovani et al. Trends in Ecology and Evolution, Vol 28 No 9, 500-501, September 2013. (DOI:10.1016/j.tree.2013.06.007)
T Burghardt, D Damen, W Mayol-Cuevas, M Mirmehdi (editors). Proceedings of the 24th British Machine Vision Conference, BMVC2013. British Machine Vision Association (BMVA), ISBN 1-901725-49-9, BMVA Press. September 2013.
(BMVC 2013 Book of Abstracts, BMVC 2013 Website)
RC Sandwell, A Loos, T Burghardt. Synthesising Unseen Image Conditions to Enhance Classification Accuracy for Sparse Datasets: Applied to Chimpanzee Face Recognition. British Machine Vision Workshop (BMVW), BMVA Press, September 2013. (BMVW: ISBN 1-901725-50-2)
HS Kuehl, T Burghardt. Animal Biometrics: Quantifying and Detecting Phenotypic Appearance. Trends in Ecology and Evolution, Vol 28 No 7, 432-441, July 2013.
(DOI:10.1016/j.tree.2013.02.013)