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
We rely on Earth’s natural ecosystems every day. Healthy forests store carbon that would otherwise be emitted into the atmosphere. Animals and insects pollinate crops that yield food. Natural tourist destinations provide income for many low-resource communities. Protecting wild habitats helps prevent the spread of infectious diseases from animals to humans.
The health of these ecosystems depends on a varied and complex web of flora and fauna, called biodiversity. Today, we are facing a biodiversity crisis as more and more species become endangered or extinct.
Conservation efforts to monitor and protect biodiversity often involve tracking the size of a species’ population in the wild. One of the best tools we have to study wildlife populations is camera traps. Camera traps can be triggered by movement or heat, providing enormous amounts of data observing the natural world without human interference.
In 2017, researchers at The Max Planck Institute for Evolutionary Anthropology partnered with DrivenData to host a machine learning competition aimed at one of the foundational challenges in processing camera trap footage: species identification. The winning algorithm was developed into a freely available Python package called Zamba, which means forest in Lingala.
To fully automate species abundance estimation from camera trap footage, however, detection and classification are not enough. Researchers also need to know the distance between the animal and the camera trap. The probability of observing an animal depends on its depth, so depth is a key input into the best statistical models that estimate population size - a method called distance sampling (Howe et al, 2017).
Currently depth estimation requires either manual human notation or multiple camera traps in the same location, both of which are very time intensive. It takes a researcher more than 10 minutes on average to label distance for every 1 minute of video - that’s a lot of time when you have a million videos! The goal of this challenge is to build computer vision models that can automatically predict an animal’s depth, rapidly accelerating this process.
The ability to automate depth estimation from camera traps would enable conservationists to get rapid information on population changes for a wide range of species, helping to identify populations under threat, evaluate the effectiveness of different conservation strategies, and more quickly respond to the escalating threats to Earth’s ecosystems.
About the data
The competition data was collected and labelled by two research teams from the Max Planck Institute for Evolutionary Anthropology (MPI-EVA) and the Wild Chimpanzee Foundation (WCF).
- One data set is from Taï National Park, Côte d'Ivoire. Data collection and labelling was led by Dr. Noémie Capelle for her research using distance sampling to estimate animal abundance in the park and studying chimpanzee populations.
- The other data set is from the newly created Moyen-Bafing National Park, Guinea. Benjamin Debetencourt led the data collection and labelling as part of his work designing a conservation management plan for the park.
Each dataset was manually labelled using reference videos as guides to determine distance. To create the reference videos, field researchers recorded themselves walking away from each camera trap, holding up a sign every meter to indicate how far they were from the camera. (Imagine how much easier it would if be a machine could that instead!) As a result, the data for this challenge presents an exceptional resource for developing depth estimation models for wildlife conservation.
Distance sampling and camera traps
- Exploiting Depth Information for Wildlife Monitoring (Haucke & Steinhage, 2021)
- Estimating animal abundance and effort–precision relationship with camera trap distance sampling (Capelle et al, 2021)
- Overcoming the Distance Estimation Bottleneck in Camera Trap Distance Sampling (Haucke et al, 2021)
- Validating camera trap distance sampling for chimpanzees (Cappelle et al, 2019)
- Distance sampling with camera traps (Howe et al, 2017)
- MathWorks: Depth Estimation in the Wild (Goel, 2021)
- Vision Transformers for Dense Prediction (Ranftl et al, 2021), also on Github
- Unsupervised Monocular Depth Learning in Dynamic Scenes (Li et al, 2020), also on Github
- Digging Into Self-Supervised Monocular Depth Estimation (Godard et al, 2019), also on Github
- Papers with Code: Monocular Depth Estimation
Deep Chimpact: Depth Estimation for Wildlife Conservation: Rules and Terms of Data Use
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DrivenData, Inc., Competition Sponsor, and the employees, officers, directors, shareholders, agents, representatives, affiliates, subsidiaries, and advertising, promotional and legal advisors thereof (collectively, the “Released Parties”) are not responsible and shall not be liable for: (i) late, lost, delayed, damaged, misdirected, incomplete, illegible, or unintelligible entries; (ii) telephone, electronic, hardware, or software program, network, Internet, or computer malfunctions, failures, or difficulties; (iii) errors in transmission; (iv) any condition caused by events beyond the control of the Released Parties that may cause the Competition to be disrupted or corrupted; (v) any injuries, losses, or damages of any kind caused by a prize or resulting from acceptance, possession, or use of a prize, or from participation in the Competition (however (but only if required by law in your jurisdiction), this release and hold harmless commitment does not apply to cases of bodily injury or loss of life to the extent that any death or personal injury is caused by the negligence of DrivenData, Competition Sponsor, or other third parties, where liability to the injured party cannot be excluded by law); or (vi) any printing or typographical errors in any materials associated with the Competition.
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.