Hi Michelle,

Yes! Pepperwood (Santa Rosa, CA) has a protocol for wildlife camera data collection and management, which we developed for Wildlife Picture Index (WPI) grids. We manage 40 cameras that run continuously, and keeping that data stream moving smoothly would be a challenge without a set process in place.

I've heard a consistent message from other camera managers: cameras collect a LOT of image data, and managing it is time-consuming and labor-intensive. This means is gigabytes of wildlife photos are imprisoned in hard drives. In a campaign to liberate these data, I've revised Pepperwood's protocol to further streamline the workflow and automate several of the "data QC" steps (which can be more time consuming than actually cataloging the images) using R scripts and wildlife photo management platform. I'm sharing our revamped protocol as a draft with colleagues to get feedback about usability. Although it's a working version, it may be helpful -- at the very least you'd have one example of how others are navigating the stormy seas of uncataloged data heaps.

If you'd like to chat further, let me know!

-Morgan

www.pepperwoodpreserve.org

Hello Michelle, I've developed a system to automatically retrieve images from Camer Traps, identify elephants from other images and store them in separate Folders. It will then transmit these Folders through my Propetiory Wireless Intranet to a Host even 100 Km away.

I use this system in India as an Elephant Early Warning System and also to collect data from remotely located Camera Traps.

A Block Diagram of this system is attached for your perusal and a video clip of this system working is available at :  Camera Trap AI Elephant Detection & Remote Data Retrieval System: https://youtu.be/wP0qpRVh1BU

I am currently working on identifying Tigers & Leopards.   

Do check if this is helpful to you.

For any clarifications, please call me on: +91-9843170559 (WhatsApp) or mail me at: [email protected] 

Thank you.

Tim Vedanayagam

Hi Michelle,

I had a group of undergrads help me with a 40,000-image dataset a few years back. We used the TEAM network Wild.ID program, so each photo that was tagged indicated who tagged it. That was helpful for checking quality later on. For our common, unmistakeable species (e.g. whitetail deer), I didn't require a second identification, but for more challenging groups (foxes, mustelids), I would often have a second person review the ID, or do it myself. Later on, I had a student go through all the tagged images of a particular species (gray squirrel, etc.) and verify the first ID. I found that some of the undergrads were very reliable in their ability to ID the species, whereas some other students needed to have their work checked more meticulously. I later thought of the idea of building a training set of say, 100 photos, to have each student run through to get a sense for their familiarity with the species, but also their ability to handle the more tricky scenarios that come up often in camtrap datasets.

Most folks could only handle 1-1.5 hours of continuous tagging. I had a few enthusiasts who would go for 2 hours straight, but that was rare. We logged effort in a shared google spreadsheet, where the students noted the dataset they worked on, any issues that came up, and any individual images that needed a second check.

I also tried to set up a more ergonomic workstation for folks (multiple monitors raised up, ergonomic mouse, etc.). Since the motion is so repetetive, easy for folks to develop carpal tunnel syndrome.

If you are dealing with a much larger dataset, you might want to look into more sophisticated AI/automation methods, but for a smaller project, this was doable. If you have a university connection, you can often recruit folks through chapter groups of The Wildlife Society. Student are often eager to gain experience, although many don't stick with it once they find out how unglamorous it is!

Good luck!

-Andy