Hey there community! Im new here and looking after lots of answers too! ;-)

We are searching aswell for the most ideal App / AI technology to ID different cats, but also other mammals if possible 

- Panthera onca

- Leopardus wiedii

- Leopardus pardalis

and if possible:

- Puma concolor

- Puma yagouaroundi

- Leopardus colocolo

- Tapirus terrestris

Every recommendation is very welcome, thanks!

Sam

Hi all! I'm part of a Pollinator Monitoring Program at California State University, San Marcos which was started by a colleague lecturer of mine who was interested in learning more about the efficacy of pollinator gardens. It grew to include comparing local natural habitat of the Coastal Sage Scrub and I was initially brought on board to assist with data analysis, data management, etc. We then pivoted to the idea of using camera traps and AI for insect detection in place of the in-person monitoring approach (for increasing data and adding a cool tech angle to the effort, given it is of interest to local community partners that have pollinator gardens). 

The group heavily involves students as researchers, and they are instrumental to the projects. We have settled on a combination of video footage and development of deep neural networks using the cloud-hosted video track detection tool, VIAME (developed by Kitware for NOAA Fisheries originally for fish track detection). Students built our first two PICTs (low-cost camera traps), and annotated the data from our pilot study that we are currently starting the process of network development for. Here's a cool pic of the easy-to-use interface that students use when annotating data: 

                                                    Figure 1: VIAME software demonstrating annotation of the track of an insect in the video (red box). Annotations are                                                           done manually to develop a neural network for the automated processing.

The goal of the group's camera trap team is develop a neural network that can track insect pollinators associated with a wide variety of plants, and to use this information to collect large datasets to better understand the pollinator occurrence and activities with local habitats. This ultimately relates to native habitat health and can be used for long-term tracking of changes in the ecosystem, with the idea that knowledge of pollinators may inform resources and conservation managers, as well as local organizations in their land use practices. We ultimately are interested in working with the Kitware folks further to not only develop a robust network (and share broadly of course!), but also to customize the data extraction from automated tracks to include automated species/species group identification and information on interaction rate by those pollinators. We would love any suggestions for appropriate proposals to apply to, as well as any information/suggestions regarding the PICT camera or suggestions on methods. We are looking to include night time data collection at some point as well and are aware the near infrared is advised, but would appreciate any thoughts/advice on that avenue as well. 

We will of course post when we have more results and look forward to hearing more about all the interesting projects happening in this space!

Cheers, 
Liz Ferguson 

One thing to keep in mind is that researchers often want or at least would like to get certain metadata on the tagged animals like sex, size, weight, apperent fitness etc. Without these the questions you can ask can get rather limited. Also, it will also often be highly desired to take samples like blood, hair, other tissue e.g.

In addition, there can be cases where it may be better not to tag the animal if it is not the right age group, is too small to carry the tag, seems like it is not in a good shape etc.

I think it will take quite an effort to get automated systems (capture robots) to make these decisions to a degree you can trust.

Cheers,

Lars

I always thought a tracker that attached like a slap bracelet would be sweet.

Hi Lucy

As others have mentioned, camera trap temperature readouts are inaccurate, and you have the additional problem that the camera's temperature can rise 10C if the sun shines on it.

I would also agree with the suggestion of getting the moon phase data off the internet.

Do you need to do this for just one project?  And do you use the same camera make/model for every deployment?  Or at least a finite number of camera makes/models?  If the number of camera makes/models you need to worry about is finite, even if it's large, I wouldn't try to solve this for the general case, I would just hard-code the pixel ranges where the temperature/moon information appears in each camera model, so you can crop out the relevant pixels without any fancy processing.  From there it won't be trivial, exactly, but you won't need AI. 

You may need separate pixel ranges for night/day images for each camera; I've seen cameras that capture video with different aspect ratios at night/day (or, more specifically, different aspect ratios for with-flash and no-flash images).  If you need to determine whether an image is grayscale/color (i.e., flash/no-flash), I have a simple heuristic function for this that works pretty well.

Assuming you can manually define the relevant pixel ranges, which should just take a few minutes if it's less than a few dozen camera models, I would extract the first frame of each video to an image, then crop out the temperature/moon pixels.

Once you've cropped out the temperature/moon information, for the temperature, I would recommend using PyTesseract (an OCR library) to read the characters.  For the moon information... I would either have a small library of images for all the possible moon phases for each model, and match new images against those, or maybe - depending on the exact style they use - you could just, e.g., count the total number of white/dark pixels in that cropped moon image, and have a table that maps "percentage of white pixels" to a moon phase.  For all the cameras I've seen with a moon phase icon, this would work fine, and would be less work than a template matching approach.

FYI I recently wrote a function to do datetime extraction from camera trap images (it would work for video frames too), but there I was trying to handle the general case where I couldn't hard-code a pixel range.  That task was both easier and harder than what you're doing here: harder because I was trying to make it work for future, unknown cameras, but easier because datetimes are relatively predictable strings, so you know when you find one, compared to, e.g., moon phase icons.

In fact maybe - as others have suggested - extracting the moon phase from pixels is unnecessary if you can extract datetimes (either from pixels or from metadata, if your metadata is reliable).

camtrapR has a function that does what you want. i have not used it myself but it seems straightforward to use and it can run across directories of images:

https://jniedballa.github.io/camtrapR/reference/OCRdataFields.html

Star-Oddi in Iceland comes to mind, but I'm not 100% sure. 

Hi Ninying,

One benefit of the Pinpoint tags is that they are user-rechargeable, something pretty much unheard of for satellite tags for decades!  If you can recover the tags, you might be able to achieve a larger samples size with fewer tags (less $$) by redeploying the recovered tags - without the costs of having the manufacturer refurbish them.

cheers,

Kyler