The U.S. Department of State’s Bureau of Oceans and International Environmental and Scientific Affairs is pleased to announce the Zoohackathon 2017 global winner, London’s Team ODINN.

What is Zoohackathon?

This fall, hundreds of participants competed around the world to develop conservation technology solutions at the second-annual Zoohackathon.

Teams were comprised of coders, designers, project managers, and conservation specialists.

Each team selected a problem statement supplied by U.S. government agencies and their partners to solve.

The Zoohackathon program promotes understanding of the problem of wildlife trafficking and enlists new partners to combat it by developing practical and innovative conservation technology solutions.

Team ODINN is a four-person team that participated in the London Zoohackathon, which was co-hosted by the Zoological Society of London (ZSL) and U.S. Embassy London.

Team ODINN’s prototype aims to reduce wildlife poaching and illegal trade by improving the effectiveness of field-based camera traps.

Currently, wildlife camera traps capture countless images of all kinds of wildlife, limiting their utility for rangers.

Team ODINN’s technology acts as an image filter that flags humans and endangered wildlife from the multitude of images, thus enabling rangers to rapidly identify poaching sites.

The prototype reduces the number of images that the rangers must examine by 98 percent and can be retro-fitted to existing camera traps, eliminating the need for additional equipment.

(The impact of illegal wildlife trade has reached unprecedented levels. With your help, we can develop solutions to reduce demand for wildlife products. Join the Zoohackathon 2018! Courtesy of ZSL – Zoological Society of London and YouTube. Posted on Sep 22, 2017)

Onsite Dynamic Identification Neural Network (ODINN)

Inspiration

ODINN discovered that camera traps used to alert rangers to the presence of poachers were being triggered by animals so often that the ranger base stations were overwhelmed with images to review.

This means that poachers can reach their targets before they can be identified by rangers.

Sometimes the images they are caught in may never even be seen.

What it does

The ODINN prototype is tailored to anti-elephant poaching efforts and is loaded directly onto the camera traps.

It acts as a form of onsite triage identifying humans and elephants, alerting the rangers only to photos that require their attention, freeing up resources for more important work.

Considering the number of times the cameras are accidentally triggered this reduces the number of images that the rangers need to check by 98%.

ODINN can be retro-fitted to existing camera traps, meaning there is no outlay for additional infrastructure or equipment.

When deployed in the field ODINN works straight away requiring no training time.

(Learn More about Team ODINN. Revolutionizing wildlife protection using cutting edge image recognition technology. Courtesy of Jonathan Bourne and YouTube. Posted on Oct 9, 2017)

ODINN Also Improves Over Time as it Collects More Images.

Because ODINN identifies the locations of both humans and elephants, rangers are able to use their knowledge of tracking, poaching techniques and the local area to plan a coordinated intercept at an appropriate location.

This tactical advantage will help protect the rangers as well as getting the rangers to the poachers before the poachers get to the elephants. 

We believe that it is important to combine high-tech, simple-to-use solutions with human intelligence and good old fashioned police work.

Unfortunately where there is money there can also be corruption, ODINN helps reduce the role of corruption in illegal wildlife trafficking by automatically tagging time stamped images, making the system significantly more robust to human manipulation.

How ODINN Built It

We used 10,000 images from camera traps in the Serengeti/Tanzania.

These images were labelled with the content of the image.

ODINN took 2000 images of elephants, 3000 images of humans and 5000 images which were either empty or contained other animals.

They next ran these 10,000 images through a neural network to identify key features of the images before using these features to identify which class (Human, Elephant, Other) the image falls into.

If the image is Human or Elephant, an alert is sent to the command center so that the image can be reviewed.

Challenges ODINN Ran Into

• ODINN had a lot of data problems to begin with, but we could solve these by working closely with the organizers explaining the issues and potential solutions.
  • Creating the hardware prototype was very challenging in the time period.
  • However, during the course of the Hackathon ODINN created a working model camera trap using the same arduino boards used in the field.
  • The model cost about £30 ($40), weighed less than a kilo and was easily hidden.
  • In practice the algorithm is loaded directly onto the arduino which makes up the camera hardware, allowing onsite triage.

Accomplishments That They’re Proud Of

• Despite being a prototype ODINN correctly flags 78% of elephants and 93% of humans
• The effect is that ODINN reduces the amount of images that rangers need to look at by 98%
• The team made use of the diverse skill sets within the group to create a revolutionary product in a short time frame
• ODINN can be deployed on camera traps already in the field and requires no investment in new equipment.

What ODINN Learned

In projects that involve many different segments, communication between team members is key to a successful product.

However, in addition to these project issues, it is important to consider how these technologies interact with the existing methods and practices already in place in the field.

ODINN consulted the experts available during the hackathon to learn from their field experience how best to develop ODINN for maximum impact.

What’s Next for ODINN

• Increase the training data to improve the accuracy
• Expand to include a range of species
• Analysis module that tracks the movement of animals enabling predicting where the animals will be even without a camera trap being triggered.

To learn More about ODINN, please visit https://devpost.com/software/o-d-i-n-n.

(Learn More about Zoohackathon. Courtesy of USA Pavilion IUCN World Conservation Congress and YouTube. Posted on Sep 2, 2016)

Be part of the solution!

Participants

In anticipation of Zoohackathon 2018 (dates and location will be announced midyear), you’ll have the chance to join a team and work to solve a wildlife conservation challenge.

Experience a one-of-a-kind opportunity to engage with experts who work to protect endangered species every day.

At the end of the hackathon, your team will present your solution to a panel of distinguished judges for the chance to move on to the global competition along with the other participating cities.

A grand prize will be awarded at the global competition, and local prizes will be awarded at each host site.

Hosts and Sponsors

Whether you would like to host or sponsor a zoohackathon, they want to hear from you!

Contact zoohackathon@state.gov to see how you can get involved in the upcoming events.