Details of Award
NERC Reference : NE/I000852/1
Video image recognition for ecological monitoring
Grant Award
- Principal Investigator:
- Professor T Tregenza, University of Exeter, Biosciences
- Co-Investigator:
- Professor R Everson, University of Exeter, Computer Science
- Grant held at:
- University of Exeter, Biosciences
- Science Area:
- Terrestrial
- Marine
- Freshwater
- Overall Classification:
- Terrestrial
- ENRIs:
- Natural Resource Management
- Global Change
- Biodiversity
- Science Topics:
- Population Genetics/Evolution
- Conservation Ecology
- Population Ecology
- Behavioural Ecology
- Abstract:
- Advances in video technology over the last decade mean it is now feasible to study insects and other small animals in their natural habitats. By tagging and genotyping individuals, their lives can be observed and the relationships between individuals, including how many offspring they leave can be quantified. The main bottleneck preventing a range of fantastic new studies is the time it takes to collect basic data from digital video recordings. We will develop new software to automate individual recognition of tagged invertebrates and other small animals in the wild. The project is a collaboration between the School of Biosciences and the School of Engineering, Computing & Mathematics (SECAM) at the University of Exeter. In Biosciences we have developed an 80 camera integrated network of video cameras monitoring a tagged population of wild crickets in a field in Spain. This provides a perfect platform for the development of a system that will have potential applications across field biology. In SECAM we have developed new approaches to object recognition and video tracking that are ideally suited to the requirements of these new monitoring techniques. This technology has the potential to catalyse a host of new studies that will ultimately provide insights into the natural ecology of invertebrates, essential for understanding ecosystems. Potential applications The availability of internet protocol (IP) CCTV cameras, which are flexible to deploy and can be wireless, has enormous potential for use in environmental research. Initially species that predictably visit small areas such as nests, flowers and burrow entrances will be the main subjects of study, but higher resolution cameras enable larger areas to be monitored. Many of our potential end users will not yet be aware of the potential studies they could be carrying out. We anticipate our software will allow behavioural ecologists to monitor the behaviour of beetles, allow pollination studies that automatically track which individual bees have visited which real or artificial flowers, and will allow studies of how the spatial distribution of terrestrial and marine gastropods arises from individual movement patterns. Technology We propose to develop new pattern recognition techniques that will allow any researcher working on a system in which individual animals are tagged to automate key aspects of data collection and analysis. We will use our database of 100,000+ hours of recordings of tagged crickets as a development platform. The crickets have tags with a 2 character code that can be seen on the video recordings. We will use image segmentation via deformable object contours and tracking to automatically distinguish and follow individuals. Variational Bayesian methods will permit efficient handling of multi-modal densities, enabling us to track more than one individual and distinguish animals from noise such as the shadows of grass moving in the breeze. Character recognition will be used to identify individuals from their tags; as these are often not visible or are obliquely viewed, we shall fuse estimates from tracked frames to provide an optimal estimate. We shall also investigate the use of two dimensional error-correcting barcodes (e.g., QR codes) as a robust alternative to alphanumeric codes. By the end of the project we expect the system to be operational in the field and available to researchers for testing and for further development - i.e. at TRL level 4.
- NERC Reference:
- NE/I000852/1
- Grant Stage:
- Completed
- Scheme:
- Directed (Research Programmes)
- Grant Status:
- Closed
- Programme:
- Tech Proof of Concept
This grant award has a total value of £96,733
FDAB - Financial Details (Award breakdown by headings)
| DI - Other Costs | Indirect - Indirect Costs | DA - Investigators | DI - Staff | DA - Estate Costs | DA - Other Directly Allocated | DI - T&S |
|---|---|---|---|---|---|---|
| £15,491 | £32,332 | £6,425 | £27,104 | £7,031 | £6,847 | £1,503 |
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