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What are Digital Still Camera and Wildlife Photo Identification Technologies?

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What are Digital Still Camera and Wildlife Photo Identification Technologies?

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Wildlife-Photo Identification is a technique used into identify and track individuals of a wild animal study population over time. It relies on capturing photographs of distinctive characteristics such as skin patterns and scars from the animal. Photo-identification allows individual recognition of animal species based on natural marks, being an alternative to other more stressful artificial tagging/marking techniques (Data-Corte 2016).
Photo-ID Conservation Research: North Gulf Oceanic Society currently uses this TPR to track movement of Killer Whale pods and to better understand their biology.

http://www.whalesalaska.org/about.htm

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How has this TPR helped conservation efforts?

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How has this TPR helped conservation efforts?

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Bird of Prey monitoring study: Monitored nests of the Goshawk (Accipiter gentilis), the Common Buzzard (Buteo buteo) and the Rough-legged Buzzard (Buteo lagopus) with a set of video surveillance equipment and a movie camera.
The aim of the study was to check existing long-term data on the diet of the Goshawk and the Common Buzzard obtained by collection of uneaten prey remains and pellets that are known to give a biased estimation of raptor diet by surveillance (Reif 2006).
How has this helped conservation?
By deciphering nest biology for 3 species of birds of prey. It is difficult to obtain scientific information on nesting culture of many birds. Thanks to this technology, scientists were able to obtain diet information for nesting birds of prey.

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1. Non-intrusive form of science for subjects!
2. Inexpensive form of science!

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1. Digital identication (i.e. photographic identication) of individual animals is a non-intrusive way to obtain information on life-history traits that is based on dis- tinctive individual morphological characteristics (such as patterns of ornamentation or morphology) that remain unchanged over time despite skin moulting (Sacchi 2016).
2. Digital identication has the advantage of being relatively inexpensive (entailing only a digital camera and computer)

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Feb 12, 2019 08:10 AM EST

A new automated method to prepare digital photos for analysis will help wildlife researchers who depend on photographs to identify individual animals by their unique markings. A wildlife biologist from Penn State teamed up with scientists from Microsoft Azure, a cloud computing service, using machine learning technology to improve how photographs are turned into usable data for wildlife research. A paper describing the new technique appears online in the journal Ecological Informatics.

"Many researchers need to identify and collect data on specific individuals in their work, for example to estimate survival, reproduction, and movement," said Derek Lee, associate research professor of biology at Penn State and principal scientist of the Wild Nature Institute. "Instead of tags and other human-applied markings that could interfere with the animal's behavior, many researchers take photographs of the animal's unique markings. We have pattern recognition software to help analyze these photos, but the photos all have to be manually prepared for analysis. Because we often have thousands of photos to go thorough, this creates a serious bottleneck."

Lee uses photographs as part of a large ongoing study to understand births, deaths, and movement of more than 3,000 giraffes in East Africa. He and his team take digital photographs of each animal's unique and unchanging spot patterns to identify them throughout their lives. But before the images can be processed by pattern recognition software to identify individuals, the research team has to manually crop each photo or delineate an area of interest. Lee collaborated with scientists from Microsoft, who have provided a new image processing service to automate this essential and time-consuming process using machine learning technology deployed on the Microsoft Azure cloud.

Using a computer algorithm for object detection, the Microsoft team trained a program to recognize giraffe torsos using existing photos that had been annotated by hand. The program was iteratively improved using an active learning process, where the system showed predicted cropping squares on new images to a human who could quickly verify or correct the results. These new images were then fed back into the training algorithm to further update and improve the program. The resulting system identifies the location of giraffe torsos in images with a very high accuracy, even when the giraffe is a small portion of the image or its torso is partially blocked by vegetation.

"The system achieves near-perfect recognition of giraffe torsos without expensive hardware requirements like a dedicated high-powered graphics processing unit," said Lee. "It is wonderful how the Azure team automated this tedious aspect of our work. It used to take us a week to process our new images after a survey, and now it is done in minutes. This system moves us closer to fully automatic animal identification from photos."

The new system will dramatically accelerate Lee's research on giraffe populations, which have rapidly declined across Africa due to habitat loss and illegal killing for meat.

"Giraffe are big animals, and they cover big distances, so naturally we are using big data to learn where they are doing well, where they are not, and why, so we can protect and connect the areas important to giraffe conservation," said Lee. "We needed new tools to accomplish this, and there was a harmonic conjunction with the Azure technology that made our work possible."

This process will also be useful to researchers studying other animals with unique identifying patterns--including some wild cats, elephants, salamanders, fish, penguins, and marine mammals. The system could be trained to identify and crop a photo to a region of interest specific to these species.

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Toward automated animal identification in wildlife research

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A new automated method to prepare digital photos for analysis will help wildlife researchers who depend on photographs to identify individual animals by their unique markings. A wildlife biologist from Penn State teamed up with scientists from Microsoft Azure, a cloud computing service, using machine learning technology to improve how photographs are turned into usable data for wildlife research.

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1. Choosing the right camera. (Will this camera's speed be first enough? Will the shutter capture stable images?)
2. Time

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1. The choice of camera and lens depends on the subject’s size, species, and fieldwork conditions. However, the greatest possible image resolution is always preferable.
2. A major constraint involved in this process is the large amount of time required for image acquisition and comparison, which is likely to increase exponentially with increasing sample size. Indeed, for small catalogues (i.e. <50 individuals), image matching is still feasible by eye. When catalogue items exceed 100 or so, however, the identi cation process becomes hugely time-consuming and misclassi cation errors occur frequently (Sacchi 2016).

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About North Gulf Oceanic Society. (n.d.). Retrieved from http://www.whalesalaska.org/about.htm

Dala-Corte, R. B., Moschetta, J. B., & Becker, F. G. (2016). Photo-identification as a technique for recognition of individual fish: a test with the freshwater armored catfish Rineloricaria aequalicuspis Reis & Cardoso, 2001 (Siluriformes: Loricariidae). Neotropical Ichthyology, 14(1). doi: 10.1590/1982-0224-20150074

Reif, V., & Tornberg, R. (2006). Using time-lapse digital video recording for a nesting study of birds of prey. European Journal of Wildlife Research, 52(4), 251–258. doi: 10.1007/s10344-006-0039-1

Sacchi, R., Scali, S., Mangiacotti, M., Sannolo, M., & Zuffi, M. A. (2016). Digital identification and analysis. Reptile Ecology and Conservation, 59–72. doi: 10.1093/acprof:oso/9780198726135.003.0005