Category: Project

Posts that refer to projects, consortia or collaborations are assigned to this category.

Great Ape Detection and Behaviour Recognition

Posted on by Tilo Burghardt

Tilo Burghardt, X Yang, F Sakib, M Mirmehdi

The problem of visually identifying the presence and locations of animal species filmed in natural habitats is of central importance for automating the interpretation of large-scale camera trap imagery. This is particularly challenging in scenarios where lighting is difficult, backgrounds are non-static, and major occlusions, image noise, as well as animal camouflage effects occur: filming great apes viacamera traps in jungle environments constitutes one such setting. Finding animals under these conditions and classifying their behaviours are important tasks in order to exploit the filmed material for conservation or biological modelling.

Together with researchers from various institutions including the Max Planck Institute for Evolutionary Anthropology we developed deep learning systems for detecting great apes in challenging imagery in the first place and for identifying animal behaviours exhibited in these camera trap clips once apes have been recognised.


Captions: (top) System Overview for CamTrap Detector. (middle and bottom) Behaviour Recognition Examples, note that the PanAfrican Programme owns the video copyrights.

Acknowledgements: All Copyright of all Images and Videos resides with the PanAfrican Programme at the MPI. We thank them for allowing to use their data for publishing our technical engineering work. We would like to thank the entire team of the Pan African Programme: ‘The Cultured Chimpanzee’ and its collaborators for allowing the use of their data. Please contact the copyright holder Pan African Programme at http://panafrican.eva.mpg.de to obtain the videos used. Particularly, we thank: H Kuehl, C Boesch, M Arandjelovic, and P Dieguez. We would also like to thank: K Zuberbuehler, K Corogenes, E Normand, V Vergnes, A Meier, J Lapuente, D Dowd, S Jones, V Leinert, EWessling, H Eshuis, K Langergraber, S Angedakin, S Marrocoli, K Dierks, T C Hicks, J Hart, K Lee, and M Murai.
Thanks also to the team at https://www.chimpandsee.org. The work that allowed for the collection of the dataset was funded by the Max Planck Society, Max Planck Society Innovation Fund, and Heinz L. Krekeler.
In this respect we would also like to thank: Foundation Ministre de la Recherche Scientifique, and Ministre des Eaux et Forłts in Cote d’Ivoire; Institut Congolais pour la Conservation de la Nature and Ministre de la Recherch Scientifique in DR Congo; Forestry Development Authority in Liberia; Direction des Eaux, Forłts Chasses et de la Conser- vation des Sols, Senegal; and Uganda National Council for Science and Technology, Uganda Wildlife Authority, National Forestry Authority in Uganda.

Related Publications

F Sakib, T Burghardt. Visual Recognition of Great Ape Behaviours in the Wild. In press. Proc. 25th International Conference on Pattern Recognition (ICPR) Workshop on Visual Observation and Analysis of Vertebrate And Insect Behavior (VAIB), January 2021. (Arxiv PDF)

X Yang, M Mirmehdi, T Burghardt. Great Ape Detection in Challenging Jungle Camera Trap Footage via Attention-Based Spatial and Temporal Feature Blending. Computer Vision for Wildlife Conservation (CVWC) Workshop at IEEE International Conference of Computer Vision (ICCVW), pp. 255-262, October 2019. (DOI:10.1109/ICCVW.2019.00034), (CVF Version), (Arxiv PDF), (Dataset PanAfrican2019 Video), (Dataset PanAfrican2019 Annotations and Code)

Fin Identification of Great White Sharks

Posted on by Tilo Burghardt

Tilo Burghardt, Ben Hughes

Recognising individuals repeatedly over time is a basic requirement for field-based ecology and related marine sciences. In scenarios where photographic capture is feasible and animals are visually unique, biometric computer vision offers a non-invasive identification paradigm.

In this line of work we developed the first fully automated biometric ID system for individual animals based on visual body contours. We applied the techniques to great white shark identification. The work was selected as one of the top 10 BMVC’15 papers and subsequently published in IJCV. The work was collaborative with NGO SaveOurSeas Foundation (SoSF) who employed Ben Hughes to extend and apply this work. The system is now being exploited at large scale by SoSF.

Related Publications

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)


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)


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, pp. 8.1-8.8. BMVA Press, September 2015. (DOI:10.5244/CW.29.MVAB.8), (Dataset FinsScholl2456)

Friesian Cattle Identification

Posted on by Tilo Burghardt

Tilo Burghardt, Will Andrew, Jing Gao, Neill Campbell, Andrew Dowsey, S Hannuna, Colin Greatwood

Holstein Friesian cattle are the highest milk-yielding bovine type; they are economically important and especially prevalent within the UK. Identification and traceability of these cattle is not only required by exportand consumer demands, but in fact many countries have introduced legally mandatory frameworks.

This line of work has shown that robust individual Holstein Friesian cattle identification can be implemented automatically and non-intrusively using computer vision pipelines fuelled by architectures utilising deep neural networks. In essence, the systems biometrically interpret the unique black-and-white coat markings to identify individual animals robustly; identification can for instance happen via fixed in-barn cameras or via drones in the field.

This work is being conducted with the Farscope CDT, VILab and BVS.

Example Training Set of a Small Herd

Related Publications

W Andrew, C Greatwood, T Burghardt. Aerial Animal Biometrics: Individual Friesian Cattle Recovery and Visual Identification via an Autonomous UAV with Onboard Deep Inference. 32nd IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 237-243, November 2019. (DOI:10.1109/IROS40897.2019.8968555), (Arxiv PDF), (CVF Extended Abstract at WACVW2020)


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)


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)


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)

Great Ape Facial Recognition and Identification

Posted on by Tilo Burghardt

Tilo Burghardt, O Brookes, CA Brust, M Groenenberg, C Kaeding, HS Kuehl, M Manguette, J Denzler, AS Crunchant, M Egerer, A Loos, K Zuberbuehler, K Corogenes, V Leinert, L Kulik

In order to evaluate the status of great ape populations and the effectiveness of conservation interventions accurate monitoring tools are needed. The utilisation and interpretation of field photography and inexpensive autonomous cameras can often provide detailed information about species presence, abundance, behaviour, welfare or population dynamics.

Caption: The figure depicts row-by-row left-to-right the gorillas: Ayana, Kukuena, Kala, Touni, Afia, Kera from Bristol Zoo. The large image on the far right is of Jock. All gorillas have unique facial features.

Together with researchers from various institutions including the Max Planck Institute for Evolutionary Anthropology, the University of Jena, and Bristol Zoo Gardens, we co-developed various computer vision and deep learning systems for detecting great ape faces in imagery and for identifying individual animals based on their unique facial features. These techniques can be applied in the wild using camera traps or manual photography, or in captive setting for studying welfare and behaviours. We are also working with the Gorilla Game Lab to utilise this technology.

Caption: Recognition example of Kera at Bristol Zoo.

Related Publications

O Brookes, T Burghardt. A Dataset and Application for Facial Recognition of Individual Gorillas in Zoo Environments. In press. Proc. 25th International Conference on Pattern Recognition (ICPR) Workshop on Visual Observation and Analysis of Vertebrate And Insect Behavior (VAIB), January 2021. (Arxiv PDF)


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)


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)


HS Kuehl, T Burghardt. Animal Biometrics: Quantifying and Detecting Phenotypic Appearance. Trends in Ecology and Evolution, Vol 28, No 7, pp. 432-441, July 2013.
(DOI:10.1016/j.tree.2013.02.013)

VI Lab researchers working with BT to enhance the experience of live events

Posted on by webmaster

5G Edge XR AR Dance

Press release, 8 October 2020

Computer vision experts from the University of Bristol are part of a new consortium, led by BT , that is driving the technology that will revolutionise the way we consume live events, from sports such as MotoGP and boxing, to dance classes.

The 5G Edge-XR project, one of seven projects funded by the Department for Digital, Culture, Media & Sport (DCMS) as part its 5G Create programme, aims to demonstrate new exciting ways that live sport and arts can be delivered remotely using immersive Virtual and Augmented Reality (VR/AR) technology combined with the new 5G network and advanced edge computing.

The 5G Edge-XR consortium, which is led by BT, also includes; The GRID Factory, Condense Reality, Salsa Sound, and Dance East. The project started in September 2020 and will run until March 2022, with a budget of over £4M, with £1.5M coming from DCMS.

The University of Bristol team is based in the Visual Information Lab (VI Lab) and will be working primarily with Condense Reality (CR). The Bristol-based SME, whose CTO and CSO are both Bristol graduates, has developed a state-of-the-art volumetric capture system, capable for generating live 3-D models for AR applications. This brings the prospect of viewing live sports and dance classes in 3-D and in your home, as though you were there in person.

The Bristol team is led by Professors Andrew Calway and David Bull, who will bring their expertise in computer vision and video coding to enhance the system developed by CR. They will be working with researchers from the BT Labs in Adastral Park, Suffolk, which is recognised for its global leadership in 5G research and standards development.

“This is a very exciting opportunity for the lab and our students, enabling us to engage in important research and knowledge and skills transfer to a local company, whilst at the same time being part of a national programme to showcase what is possible using AR and the new 5G technology,” said Professor Calway.

Professor Tim Whitley, BT’s MD of Applied Research, said: “The approaches we’re exploring with these teams in Bristol can transform how we experience sport, music, drama and education. With access to live cultural events and sport being limited by the ongoing pandemic, this project seems more relevant and urgent than ever.”

Nick Fellingham, CEO and Co-Founder of CR, added: “The 5G Edge-XR project is a real boost for us and working with the University will help us to push our technology to new levels, giving us that important edge in the market.”

End

Learning-optimal Deep Visual Compression

Posted on by Aaron

David Bull, Fan Zhang and Paul Hill

INTRODUCTION

Deep Learning systems offer state-of-the-art performance in image analysis, outperforming conventional methods. Such systems offer huge potential across military and commercial domains including: human/target detection and recognition and spatial localization/mapping. However, heavy computational requirements limit their exploitation in surveillance applications, particularly airborne, where low-power embedded processing and limited bandwidth are common constraints.

Our aim is to explore deep learning performance whilst reducing processing and communication overheads, by developing learning-optimal compression schemes trained in conjunction with detection networks.

ACKNOWLEDGEMENT

This work has been funded by DASA Advanced Vision 2020 Programme.