Research in the Wild: AI, Leopards and Photobombs

Our workforce primarily works on leopards and different terrestrial mammals in protected areas and different forests of Karnataka. Our analysis focuses on establishing the baseline inhabitants of leopards in each forests and human-dominated landscapes, and additional monitoring the identical areas periodically to evaluate adjustments in the inhabitants.

We survey an space of curiosity utilizing camera-traps which seize photographs of wildlife with minimal intrusion. Camera-traps are remotely triggered, motion-sensing cameras that seize a photograph each time the infrared beam is minimize both by an animal or an individual. They are comparatively gentle, simple to make use of, and low-fuss on the discipline as we need not carry a laptop computer simply to obtain knowledge from every camera-trap. Each unit has a protected USB slot the place a pen drive might be inserted and we will immediately obtain the knowledge onto the pen drive. However, every unit does must be tethered firmly to a tree or a pole lest curious younger elephants tear them away throughout play, or poachers steal them. It is attention-grabbing to notice that the unsuccessful events get captured on the very camera-traps they attempt to steal, or on the one put in proper reverse (which they miss recognizing).

Elephant calves are filled with curiosity and take pleasure in interacting with issues on the floor that they’ll contact and really feel. This infant is having an excellent time stripping the camera-trap away from the sapling it was tethered to.
Photo Credit: Sanjay Gubbi

We can simply programme the camera-traps for set off sensitivity and frequency of captures as per our requirement. The infrared sensor detects the movement of the animal thus, triggering the digicam to seize a photograph. The high quality of the images is adequate to distinguish the patterns on animals comparable to leopards and tigers which is what we’re primarily involved with. However, we do take pleasure in our share of entertaining images of macaques posing for pond-side selfies, or dholes that resemble flying corgis.

We get a number of hundreds of images from every examine web site which we initially used to manually kind and analyse relying on the species photographed. The effort of sorting the images alone typically required an infinite quantity of handbook work, and normally took us a number of months in a yr. Apart from the great amount of sources it consumed, it was a hindrance to working in extra websites. With the leopard being a widespread species, working in a bigger variety of websites was essential to ascertain benchmark knowledge for as many areas as potential. If we could not kind pictures from one web site in a manageable body of time, how would we prolong the examine past?

dhole sanjay gubbi 800 Dhole

We photo-captured this dhole in the center of a dash. We guarantee you, this isn’t a flying Corgi, nevertheless a lot it could resemble one.
Photo Credit: Sanjay Gubbi

Given the large-scale of information and variety of pictures to sift by means of, we collaborated with Mr. Ramprasad, the former chief technologist for AI at Wipro who helped design a programme that would do the picture sorting for us.

The software program makes use of a convolutional neural community (CNN), which is a framework that permits machine-learning algorithms to work collectively to analyse photographs. This type of work falls underneath an interdisciplinary discipline known as ‘pc imaginative and prescient’ which offers with coaching machines to determine and classify photographs very like a human would. The CNN classifier must be skilled to acknowledge the options, colors, shapes, sizes, and distinctive patterns related to leopards and different animals. We fed hundreds of photographs to coach the classifier to acknowledge leopards from our discipline websites with a sure measure of accuracy.

In the first stage of research, the software program helps us immensely by eradicating all the ‘noise’ – all irrelevant photographs with out the goal wild animals, or these with people or livestock. Camera-traps are sometimes triggered by the slightest movement of even falling leaves, resulting in a big portion of the photographs being false captures. As an estimate from our largest web site in 2018, out of a complete of two,99,364 photographs captured, solely about 6% (17,888) of the photographs obtained have been of mammals, with the remainder of the 94% being people, livestock, different species and false triggers.

leopard sanjay gubbi 800 leopard

Most images we get are of animals strolling by – half blurred or partial. This leopard was type sufficient to sit down and pose for our camera-trap.
Photo Credit: Sanjay Gubbi

For the second stage, we skilled the classifier to determine and segregate the animal photographs as per the mammalian species we give attention to. The classifier at the moment operates at an accuracy of round 90% for giant cat (leopards and tigers) identification. Its accuracy will go up by studying extra traits of these goal species as we feed extra images from comparable habitats into the software program. This accuracy is extremely helpful as many photographs we receive are partials with just some physique elements, or with obscured patterns, at completely different angles, or captured at evening or in poor lighting. Currently, the accuracy of the classifier for sure distinct species comparable to leopards, tigers, and porcupines is greater than different species comparable to sambar deer, dhole, and so forth. We can treatment this by coaching it with extra and various photographs of those species.

To date, we have used this software program to kind by means of greater than 1.6 million images to determine 363 leopard people. With this software program, our workload has lowered from months to hours. The monumental effort we might have in any other case put into sifting by means of these many photographs manually has been minimize down massively. To put into perspective, the classifier can course of as much as 60,000 photographs in almost half the time required by three researchers working full-time for 3 weeks, saving us a whole lot of invaluable time and effort.

leopard and tiger sanjay gubbi 800 leopard and tiger

Tiger and leopard people might be differentiated primarily based on the distinctive patterns on their our bodies. Notice how the stripes differ amongst the tigers alongside the flanks, stomach, undersides and the legs. The rosettes differ between the leopards in the shapes, and the approach they’re clustered throughout the physique.
Photo Credit: Sanjay Gubbi

The closing step for us is to determine particular person leopards and tigers to estimate their inhabitants utilizing acceptable statistical methodology. For animals which have marks or patterns on their physique like the leopard or tiger, we will determine people by matching these marks or patterns as they’re distinctive to a person identical to fingerprints in people.

We evaluate the photographs of leopards and tigers which have been validated and extracted by the classifier through the use of one other software program known as Wild-ID which pulls out photographs with comparable patterns for us to match. These automated matches do have some margin of error thus, we validate the closing set of photographs manually. However, this software program nonetheless cuts down our effort of going by means of almost 900 photographs to determine round 70 people to search out the preliminary matches. Looking by means of a whole bunch of photographs of patterned animals might be extraordinarily strenuous for the eyes, additional bringing in the possibilities of human error.

We have been working in the direction of incorporating know-how and related software program into completely different elements of our work, to chop down the handbook effort and get faster outcomes. The intention is to minimise error, maximise effectivity whereas additionally optimising the human-effort part that goes into implementing a analysis examine on such a big scale.


Amrita Menon is in conservation biology and inhabitants ecology. She is at the moment working as a analysis affiliate on the leopard conservation challenge in Karnataka with the Western Ghats Programme at NCF.

Sanjay Gubbi is a conservation biologist whose work focuses on the conservation of enormous carnivores like tigers and leopards. He at the moment works as a Scientist and Programme Head with the Western Ghats Programme at Nature Conservation Foundation.

Phalguni Ranjan is a marine biologist working as a science and conservation communicator with the Western Ghats Programme at NCF.

This collection is an initiative by the Nature Conservation Foundation, underneath their programme Nature Communication to encourage nature content material in all Indian languages. If you are in writing on nature and birds, please replenish this form.


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