Deep Chimpact: Depth Estimation for Wildlife Conservation
Help conservationists monitor wildlife populations. In this challenge, you will estimate the depth of animals in camera trap videos. Better distance estimation models can rapidly accelerate the availability of critical information for wildlife monitoring and conservation. Competition hosted by MathWorks. #climate
Help conservationists monitor wildlife populations. In this challenge, you will estimate the depth of animals in camera trap videos. Better distance estimation models can rapidly accelerate the availability of critical information for wildlife monitoring and conservation.
No. of Entries
RTX 40901st Place Team
The depth estimation models and level of precision achieved by the winning teams offer massive time saving in the range of 40-60% for the processing of camera trap footage. These will help us to develop effective monitoring approaches for surveying hundreds of wildlife species.
— Hjalmar Kühl, Senior Scientist at the iDiv (German Centre for Integrative Biodiversity Research Halle-Jena-Leipzig)
To protect the Earth's natural resources amid environmental and human pressures, conservationists need to be able to monitor species population sizes and population change. Camera traps are widely used in conservation research to capture images and videos of wildlife without human interference.
Using statistical models for distance sampling, the frequency of animal sightings can be combined with the distance of each animal from the camera to estimate a species' full population size. However, getting distances from camera trap footage currently entails an extremely manual, time-intensive process. This creates a bottleneck for conservationists working to understand and protect wild animals and their ecosystems.
The goal of this challenge was to use machine learning and advances in monocular (single-lens) depth estimation techniques to automatically estimate the distance between a camera trap and an animal contained in its video footage. The challenge drew on a unique labeled dataset from research teams from the Max Planck Institute for Evolutionary Anthropology (MPI-EVA) and the Wild Chimpanzee Foundation (WCF). Participants were evaluated based on how accurately their automated algorithms could predict the distance between the camera and wildlife at each point in time.
Over the course of the competition, participants tested over 900 solutions and were able to significantly advance existing methods of depth estimation in wildlife contexts. One of the most recent studies applying machine learning to depth estimation, Overcoming the Distance Estimation Bottleneck in Estimating Animal Abundance with Camera Traps (2021), used monocular depth estimation to achieve a mean absolute error of 1.85 m. The method relied on a series of reference videos that required field researchers to travel to and visually document distances at each camera trap location.
Competitors were not provided with reference videos to more fully automate the time-intensive process. But our winners were not deterred! The top-scoring solutions improved on the state-of-the-art method, with the top model achieving a mean absolute error (MAE) of 1.62. The winning approaches were also most accurate at close distances, a useful result for conservation applications using distance sampling. These innovative models could help make depth estimation more accessible to conservationists around the world, even those without the time or resources to create reference videos.
All of the prize-winning solutions from this competition are linked below and made available on for anyone to use and learn from. The project team is now working to apply these solutions in connection with the open-source Project Zamba, a computer vision repository for wildlife conservation.
Deep Chimpact: Depth Estimation for Wildlife Conservation: Rules and Terms of Data Use
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Private sharing of code
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Disclaimer of Warranties
You understand that we cannot and do not guarantee or warrant that the Data or other files available for downloading from the internet or the Competition Website will be free of viruses or other destructive code. You are responsible for implementing sufficient procedures and checkpoints to satisfy your particular requirements for anti-virus protection and accuracy of data input and output, and for maintaining a means external to our site for any reconstruction of any lost data. RELEASED PARTIES WILL NOT BE LIABLE FOR ANY LOSS OR DAMAGE CAUSED BY A DISTRIBUTED DENIAL-OF-SERVICE ATTACK, VIRUSES OR OTHER TECHNOLOGICALLY HARMFUL MATERIAL THAT MAY INFECT YOUR COMPUTER EQUIPMENT, COMPUTER PROGRAMS, DATA OR OTHER PROPRIETARY MATERIAL DUE TO YOUR USE OF THE WEBSITE OR ANY SERVICES OR ITEMS OBTAINED THROUGH THE WEBSITE OR TO YOUR DOWNLOADING OF ANY MATERIAL POSTED ON IT, OR ON ANY WEBSITE LINKED TO IT.
YOUR USE OF THE COMPETITION WEBSITE, ITS CONTENT AND ANY SERVICES OR ITEMS OBTAINED THROUGH THE WEBSITE AND RECEIPT OF ANY PRIZES IS AT YOUR OWN RISK. THE WEBSITE, ITS CONTENT AND ANY SERVICES OR ITEMS OBTAINED THROUGH THE WEBSITE ARE PROVIDED ON AN “AS IS” AND “AS AVAILABLE” BASIS, WITHOUT ANY WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, BEYOND ANY NON-EXCLUDABLE GUARANTEES (DEFINED BELOW). NEITHER THE RELEASED PARTIES NOR ANY PERSON ASSOCIATED WITH RELEASED PARTIES MAKES ANY WARRANTY OR REPRESENTATION WITH RESPECT TO THE COMPLETENESS, SECURITY, RELIABILITY, QUALITY, ACCURACY OR AVAILABILITY OF THE COMPETITION WEBSITE. WITHOUT LIMITING THE FOREGOING, NEITHER RELEASED PARTIES NOR ANYONE ASSOCIATED WITH THE RELEASED PARTIES REPRESENTS OR WARRANTS THAT THE COMPETITION WEBSITE, ITS CONTENT OR ANY SERVICES OR ITEMS OBTAINED THROUGH THE WEBSITE WILL BE ACCURATE, RELIABLE, ERROR-FREE OR UNINTERRUPTED, THAT DEFECTS WILL BE CORRECTED, THAT OUR SITE OR THE SERVER THAT MAKES IT AVAILABLE ARE FREE OF VIRUSES OR OTHER HARMFUL COMPONENTS OR THAT THE COMPETITION WEBSITE OR ANY SERVICES OR ITEMS OBTAINED THROUGH THE WEBSITE WILL OTHERWISE MEET YOUR NEEDS OR EXPECTATIONS.
NOTHING IN THESE COMPETITION RULES LIMITS, EXCLUDES, OR MODIFIES OR PURPORTS TO LIMIT, EXCLUDE, OR MODIFY ANY STATUTORY CONSUMER GUARANTEE OR ANY IMPLIED CONDITION OR WARRANTY, THE EXCLUSION OF WHICH FROM THESE COMPETITION RULES WOULD CONTRAVENE ANY STATUTE OR CAUSE ANY PART OF THESE TERMS TO BE VOID (“NON-EXCLUDABLE GUARANTEES”). SUBJECT TO THE LIMITATIONS IN THE PRECEDING SENTENCE AND TO THE MAXIMUM EXTENT PERMITTED BY ANY MANDATORY PROVISIONS OF APPLICABLE LAW, THE RELEASED PARTIES HEREBY DISCLAIM ALL WARRANTIES OF ANY KIND, WHETHER EXPRESS OR IMPLIED, STATUTORY OR OTHERWISE, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF MERCHANTABILITY, NON-INFRINGEMENT AND FITNESS FOR PARTICULAR PURPOSE.
THE FOREGOING DOES NOT AFFECT ANY WARRANTIES WHICH CANNOT BE EXCLUDED OR LIMITED UNDER APPLICABLE LAW.
Governing Law and Jurisdiction
DrivenData shall be the sole interpreter of these Competition Rules.
All matters relating to the Competition Rules or the Competition and any dispute or claim arising therefrom or related thereto (in each case, including non-contractual disputes or claims), shall be governed by and construed in accordance with the internal laws of the Commonwealth of Massachusetts without giving effect to any choice or conflict of law provision or rule (whether of the Commonwealth of Massachusetts or any other jurisdiction).
The invalidity or unenforceability of any provision of these Competition Rules shall not affect the validity or enforceability of any other provision. In the event that any provision is determined to be invalid or otherwise unenforceable or illegal, these Competition Rules shall otherwise remain in effect and be construed in accordance with their terms as if the invalid or illegal provision was not contained herein.