| EPSRC Reference: |
EP/M02153X/1 |
| Title: |
Facial Deformable Models of Animals |
| Principal Investigator: |
TZIMIROPOULOS, Dr G |
| Other Investigators: |
|
| Researcher Co-Investigators: |
|
| Project Partners: |
|
| Department: |
School of Computer Science |
| Organisation: |
University of Nottingham |
| Scheme: |
First Grant - Revised 2009 |
| Starts: |
14 September 2015 |
Ends: |
13 December 2016 |
Value (£): |
98,600
|
| EPSRC Research Topic Classifications: |
|
| EPSRC Industrial Sector Classifications: |
| No relevance to Underpinning Sectors |
|
|
| Related Grants: |
|
| Panel History: |
| Panel Date | Panel Name | Outcome |
|
05 Mar 2015
|
EPSRC ICT Prioritisation Panel - Mar 2015
|
Announced
|
|
|
Summary on Grant Application Form |
Although the automatic monitoring of animals and their behaviour is of great importance to the field of animal health and welfare, developing computational tools for this purpose has received little attention by the scientific community. Aside the emotional value that they may have to people, animals are also important to the society and the economy, and developing such tools will be a big, transformative step with direct impact on all these areas. Towards this end, F.D.M.A. will develop novel tools for detecting and tracking animal facial behaviour, and in particular, for learning and fitting facial deformable models of animals to unconstrained images/video. Although algorithms for detecting and tracking of human faces have been recently shown capable of coping to some extent with unseen variations (e.g. pose, expression, illumination, background and occlusion), there is much more variability in the face of animals that the current solutions have not yet addressed. F.D.M.A. sets out to challenge the current state-of-the-art methods in face alignment and tracking, and develop learning and fitting algorithms that can deal with very large shape and appearance variations, typically encountered in animal faces. To the best of our knowledge, this problem has never been explored in the past by the Computer Vision community. It is significantly more difficult and different than prior work on human faces, as animal faces exhibit a much larger degree of variability in shape and appearance as well as in pose and expression.
The tools to be developed by F.D.M.A. will enable the automatic analysis and understanding of animal facial behaviour which is of growing importance to animal health and welfare. The potential benefits of enhanced animal health and welfare are great; for animals, their owners, society, public health and the economy. Cats and dogs, the two species chosen by F.D.M.A., are the most popular companion animals, worldwide and of enormous societal and economic importance. To the best of our knowledge, there is no prior work in computer vision on detecting and tracking the facial deformable shape and motion of animals in images and videos. F.D.M.A. sets out to develop such tools that will enable automatic facial animal behaviour understanding. Aside animal health and welfare, the Computer Vision tools to be developed by F.D.M.A. can be used to facilitate research in other scientific disciplines, such as Animal Behaviour, Vision and Robotics.
|
|
Key Findings |
|
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
|
|
Potential use in non-academic contexts |
|
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
|
|
Impacts |
|
| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
|
| Date Materialised |
|
|
|
|
Sectors submitted by the Researcher |
|
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
|
| Project URL: |
|
| Further Information: |
|
| Organisation Website: |
http://www.nottingham.ac.uk |