230 research outputs found
Automated Visual Fin Identification of Individual Great White Sharks
This paper discusses the automated visual identification of individual great
white sharks from dorsal fin imagery. We propose a computer vision photo ID
system and report recognition results over a database of thousands of
unconstrained fin images. To the best of our knowledge this line of work
establishes the first fully automated contour-based visual ID system in the
field of animal biometrics. The approach put forward appreciates shark fins as
textureless, flexible and partially occluded objects with an individually
characteristic shape. In order to recover animal identities from an image we
first introduce an open contour stroke model, which extends multi-scale region
segmentation to achieve robust fin detection. Secondly, we show that
combinatorial, scale-space selective fingerprinting can successfully encode fin
individuality. We then measure the species-specific distribution of visual
individuality along the fin contour via an embedding into a global `fin space'.
Exploiting this domain, we finally propose a non-linear model for individual
animal recognition and combine all approaches into a fine-grained
multi-instance framework. We provide a system evaluation, compare results to
prior work, and report performance and properties in detail.Comment: 17 pages, 16 figures. To be published in IJCV. Article replaced to
update first author contact details and to correct a Figure reference on page
Cattle Identification Using Muzzle Images and Deep Learning Techniques
Traditional animal identification methods such as ear-tagging, ear notching,
and branding have been effective but pose risks to the animal and have
scalability issues. Electrical methods offer better tracking and monitoring but
require specialized equipment and are susceptible to attacks. Biometric
identification using time-immutable dermatoglyphic features such as muzzle
prints and iris patterns is a promising solution. This project explores cattle
identification using 4923 muzzle images collected from 268 beef cattle. Two
deep learning classification models are implemented - wide ResNet50 and
VGG16\_BN and image compression is done to lower the image quality and adapt
the models to work for the African context. From the experiments run, a maximum
accuracy of 99.5\% is achieved while using the wide ResNet50 model with a
compression retaining 25\% of the original image. From the study, it is noted
that the time required by the models to train and converge as well as
recognition time are dependent on the machine used to run the model.Comment: 8 pages, 4 figures, 2 table
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Assessing rotation-invariant feature classification for automated wildebeest population counts
Accurate and on-demand animal population counts are the holy grail for wildlife conservation organizations throughout the world because they enable fast and responsive adaptive management policies. While the collection of image data from camera traps, satellites, and manned or unmanned aircraft has advanced significantly, the detection and identification of animals within images remains a major bottleneck since counting is primarily conducted by dedicated enumerators or citizen scientists. Recent developments in the field of computer vision suggest a potential resolution to this issue through the use of rotation-invariant object descriptors combined with machine learning algorithms. Here we implement an algorithm to detect and count wildebeest from aerial images collected in the Serengeti National Park in 2009 as part of the biennial wildebeest count. We find that the per image error rates are greater than, but comparable to, two separate human counts. For the total count, the algorithm is more accurate than both manual counts, suggesting that human counters have a tendency to systematically over or under count images. While the accuracy of the algorithm is not yet at an acceptable level for fully automatic counts, our results show this method is a promising avenue for further research and we highlight specific areas where future research should focus in order to develop fast and accurate enumeration of aerial count data. If combined with a bespoke image collection protocol, this approach may yield a fully automated wildebeest count in the near future
Quantifying whether different demographic models produce incongruent results on population dynamics of two long-term studied rodent species
1. Population density (ind/ha) of long-term (>15 years) series of CMR populations, using distinct demographic models designed for both open and closed populations, were analysed for two sympatric species of rodents (Myodes glareolus and Apodemus flavicollis) from a mountain area in central Italy, in order to test the relative performance of various employed demographic models. In particular, the hypothesis that enumeration models systematically underestimate the population size of a given population was tested.2. Overall, we compared the performance of 7 distinct demographic models, including both closed and open models, for each study species. Although the two species revealed remarkable intrinsic differences in demography traits (for instance, a lower propensity for being recaptured in Apodemus flavicollis), the Robust Design appeared to be the best fitting model, showing that it is the most suitable model for long-term studies.3. Among the various analysed demographic models, Jolly-Seber returned the lower estimates of population density for both species. Thus, this demographic model could not be suggested for being applied for long-term studies of small mammal populations because it tends to remarkably underestimate the effective population size. Nonetheless, yearly estimates of population density by Jolly-Seber correlated positively with yearly estimates of population density by closed population models, thus showing that interannual trends in population dynamics were uncovered by both types of demographic models, although with different values in terms of true population size
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