Quick question on this topic to take advantage of those that know a lot about it already. So once you have extracted all your camera data and are going through the AI object detection phase which identifies the animal types. What file formation that contains all of the time + location + labels in the photos data do the most people consider the most useful ? I'm imagining that it's some format that is used by the most expressive visualization software around I suppose. Is this correct ?

A quick look at the trapper format suggested to me that it's meta data from the camera traps and thus perform the AI matching phase. But it was a quick look, maybe it's something else ? Is the trapper format also for holding the labelled results ? (I might actually the asking the same question as the person that started this thread but in different words).

Another question. Right now pretty  much all camera traps trigger on either PIR sensors or small AI models. Small AI models would tend to have a limitation that they would only accurately detect animal types and recognise them at close distances where the animal is very large and I have question marks as to whether small models even in these circumstances are not going to make a lot of classification errors (I expect that they do and they are simply sorted out back at the office so to speak). PIR sensors would typically only see animals within say 6m - 10m distance. Maybe an elephant could be detected a bit further. Small animals only even closer.

But what about when camera traps can reliably see and recognise objects across a whole field, perhaps hundreds of meters?

Then in principle you don't have to deploy as many traps for a start. But I would expect you would need a different approach to how you want to report this and then visualize it as the co-ordinates of the trap itself is not going to give you much  information. We would be in a situation to potentially have much more accurate and rich biodiversity information.

Maybe it's even possible to determine to a greater degree of accuracy where several different animals from the same camera trap image are spatially located, by knowing the 3D layout of what the camera can see and the location and size of the animal.

I expect that current camera trap data formats may fall short of being able to express that information in a sufficiently useful way, considering the in principle more information available and it could be multiple co-ordinates per species for each image that needs to be registered.

I'm likely going to be confronted with this soon as the systems I build use state of the art large number of parameter models that can see species types over much greater distances. I showed in a recent discussion here, detection of a polar bear at a distance between 130-150m.

Right now I would say it's an unknown as to how much more information about species we will be able to gather with this approach as the images were not being triggered in this manner till now. Maybe it's far greater than we would expect ? We have no idea right now.

Steve and I are looking to develop a low-cost benthic drift camera with a live video feed. Our hope is to use an acoustic modem to give us a low quality feed for navigation / hazard avoidance.  This could be as simple as a small black and white image refreshed every 1 sec. What we need to know from the system is are we at the bottom? and are we about to hit an obstacle? 

Data Rate Requirements: A very low quality (360p) video stream - could be black & white and low fps to reduce bit rate requirements (hopefully 1kbps)

Range: At least depth 500m but ideally down to 2,000m (deployed below a vessel from a tether)

Environment: Deep water temperature range down to 0C. Open ocean salinity levels (33-35 psu). Limited noise apart from deployment vessel (engine & echosounder)

Power Budget: Transmitter must be powered by a battery capable of continuous working for at least 1 hour.

Cost: Ideally under £1000 (GBP) for the transmitter & receiver.

Integration: Don't know. We are hoping to plug into an arduino or equivalent.

Real-time or Delayed: We need real time transmission with very limited lag for slow moving obstacle avoidance.

It's a big ask, but any pointers would be very welcome

Small update. I uploaded >1000 videos from a spypoint flex camera and TrapTagger worked really well. Another program that I'm currently interested in is Timelapse which uses the file creation date/time. I haven't yet tried it, but it looks promising as well. 

No problem at all Yu (@JY) and good luck with your studies! Happy to discuss this and any other ideas any time.

All the best,

Rob

Hi Brett,this ocean lab sychelles is great to hear from you! I love your remote monitoring buoy project, especially the recycling of DFAD buoys and the design of custom-built ones that are full of interesting features. The expandability of the navigation buoys enabling it to be configured as hydrophones, cameras, or water quality monitors is a great feature. The technical marvels, such as recycling DFADs' circuits, making your own hydrophone, and designing a super-efficient system, are terrific. Moreover, it is encouraging to witness the deployment of your early system, particularly the live video and water quality data you have collected. You will definitely make a positive impact on the Wildlabs community with your knowledge of electrical engineering and software development. I care to hear more of your discoveries and any news that you will be sharing. Keep doing what you excel at!

It looks like the website has not been updated since 2022 and indeed you need to register. If you go under 'Manage Account' you may be able to register. I tried to register but I got an error saying 'Login failed! Account is not active'.  I got an email saying "Your account must be approved before being activated. Once your account has been approved, you will be notified." So I am waiting for the account to be approved...

I'll keep you posted