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Anika Puri

Anika Puri

2022 Davidson Fellow
$25,000 Scholarship

Age: 17
Hometown: Chappaqua, NY

Engineering: “ElSa: A Novel Real-time Wildlife Poacher Detection Solution Leveraging Machine Learning Driven Spatio-temporal Analysis of Nighttime UAV Thermal Infrared Videos”

About Anika

My name is Anika Puri, and if I were to describe myself in a few words, I would say that I am a creative, caring and hardworking girl. Growing up, I’ve always been encouraged to initialize my RAM: take Risks, Ask questions, make Mistakes. This emboldened me to fervently pursue all my passions, and put my interests into action. Whether it was presenting my Lego Mindstorms robot at the Maker Faire or discovering my love for drones and getting a commercial drone pilot license, I’ve been able to see infinite possibilities transforming curiosity into impact.

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"For me, becoming a Davidson Fellow represents an amazing opportunity to join a community of other young people passionate about STEM and its potential to make an impact in the world! "

Project Description

Wildlife poaching of endangered species, such as elephants and rhinoceroses in Africa and Asia, for illegal trading has become a biodiversity crisis (UN sustainable Development Goal SDG15). Recently, unoccupied aerial vehicles (UAVs) equipped with heat-sensing infrared cameras (and coupled with computer vision software) have been deployed to help park rangers monitor protected areas at night when illegal wildlife poaching typically occurs. To maximize the area covered within a fixed flight time and battery constraints, the UAVs usually fly at an altitude of approxamately 400 ft. This results in small animal/human sizes in the captured thermal images and, consequently, leads to poor detection accuracy as low as 20% for humans. In this research, I studied the Spatio-temporal nature of the video data, i.e., the difference in the movement pattern of animals and humans over time, such as their turning radius, speed, herd nature etc. to determine whether these features have promise in improving classification. When tested using a thermal infrared video dataset collected from four national parks in Africa, this method was able to use movement patterns to detect humans with 90.9% accuracy - a 4X improvement over the existing state-of-the-art methods. Furthermore, my low-cost ($300) design prototype ElSa (Elephant-Savior) built with commodity components mitigates the need for costly, high-resolution thermal cameras ($4,800), easing the burden on resource-constrained Parks in Africa and Asia.

Deeper Dive

Exploring technology & engineering for social good, I am especially committed to innovation focusing on endangered species & environmental science. During my trips to India, I was taken aback by the rows of ivory jewelry and statues I saw in the Bombay market, despite the 1990 global ban. I was stunned by the fact that 70% of the elephant population has died in the last 40 years, largely due to the illegal ivory trade: one elephant dies every 26 minutes at the hands of a poacher. Wildlife conservation has become one of the most important sustainability goals for our environment.

Inspired to learn more, I discovered Park rangers currently deploy drones equipped with heat-sensitive infrared imaging cameras that send live thermal video data to ranger stations. These videos must be continuously watched by technical operators, a tedious and error-prone task. Recent research has automated this process with shape detection algorithms; however, this process, too, results in low accuracy with small animal/human sizes at high altitudes. I realized that video data carries much more information than static image frames - including time-domain information capturing the Spatio-temporal movements of objects. I hypothesized that the difference in animal and human movement patterns in UAV thermal infrared video data would significantly increase the accuracy of identifying human/poacher activity in national wildlife parks. ElSa was born.

In my research, I develop a first-ever methodology, which leverages the animal and human movement (Spatio-temporal) patterns for significantly improving poacher detection accuracy in infrared thermal wildlife video data. While other methods used static image data for poacher detection, resulting in only 20% accuracy, this work is the first to harness real-time, Spatio-temporal patterns.

When tested using a thermal infrared video dataset called BIRDSAI, collected from four national parks in Africa, my algorithm detected humans (potential poachers) with over 90% accuracy. This means that my research is able to detect poachers in nighttime, infrared videos from more than 500 feet off the ground with 4X higher accuracy than the current state-of-the-art solutions.


If you could magically become fluent in any language, what would it be?

I would love to become magically fluent in the language of dogs - we can understand their facial expressions, but communicating with speech will open up a whole new exciting world.

What is your favorite hobby?

I really enjoy playing the harp and fell in love with the instrument in 3rd grade, when I saw a local harpist play at the annual holiday concert. I love to play at Senior centers and it's always an incredible feeling to see their peaceful smiles and heads bobbing to the beat.

What is one of your favorite quotes?

My favorite quote is “Nothing is impossible, the word itself says, ’I’m possible!’” from Audrey Hepburn.

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In The News

Chappaqua, N.Y. – The Davidson Fellows Scholarship Program has announced the 2022 scholarship winners. Among the honorees is 17-year-old Anika Puri of Chappaqua. Puri won a $25,000 scholarship for her project, ElSa (Elephant-Savior): A Novel Real-time Wildlife Poacher Detection Solution Leveraging Machine Learning Driven Spatio-temporal Analysis of Nighttime UAV Thermal Infrared Videos. She is one of only 21 students across the country to be recognized as a 2022 scholarship winner.

Download the full press release here