Camera traps have been a key part of the conservation toolkit for decades. Remotely triggered video or still cameras allow researchers and managers to monitor cryptic species, survey populations, and support enforcement responses by documenting illegal activities. Increasingly, machine learning is being implemented to automate the processing of data generated by camera traps.
A recent study published showed that, despite being well-established and widely used tools in conservation, progress in the development of camera traps has plateaued since the emergence of the modern model in the mid-2000s, leaving users struggling with many of the same issues they faced a decade ago. That manufacturer ratings have not improved over time, despite technological advancements, demonstrates the need for a new generation of innovative conservation camera traps. Join this group and explore existing efforts, established needs, and what next-generation camera traps might look like - including the integration of AI for data processing through initiatives like Wildlife Insights and Wild Me.
Group Highlights:
Our past Tech Tutors seasons featured multiple episodes for experienced and new camera trappers. How Do I Repair My Camera Traps? featured WILDLABS members Laure Joanny, Alistair Stewart, and Rob Appleby and featured many troubleshooting and DIY resources for common issues.
For camera trap users looking to incorporate machine learning into the data analysis process, Sara Beery's How do I get started using machine learning for my camera traps? is an incredible resource discussing the user-friendly tool MegaDetector.
And for those who are new to camera trapping, Marcella Kelly's How do I choose the right camera trap(s) based on interests, goals, and species? will help you make important decisions based on factors like species, environment, power, durability, and more.
Finally, for an in-depth conversation on camera trap hardware and software, check out the Camera Traps Virtual Meetup featuring Sara Beery, Roland Kays, and Sam Seccombe.
And while you're here, be sure to stop by the camera trap community's collaborative troubleshooting data bank, where we're compiling common problems with the goal of creating a consistent place to exchange tips and tricks!
Header photo: ACEAA-Conservacion Amazonica
Adventure Scientists is a 501(c)3 nonprofit organization based in Bozeman, MT that equips scientists and researchers with high-quality data collected from the outdoors that are crucial to addressing environmental challenges around the world.
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Wildlife Conservation Society (WCS)
I am a biologist interested in land use change impacts on biodiversity and sustainable value chains. I work with productive sectors, incorporating biodiversity conservation as a criteria for planning and managing productive systems.
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Conservation Biologist; Research interest Human Dimensions on Wildlife Conservation and Conservation Tech
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My name is Ninying Benedicta a PhD student under the Department of wildlife resource management in Cameroon. I love learning about wildlife and their habitats. I also love working with communities surrounding the forests, learning on Traditional Ecological Knowledge.
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I am both head of the Innovation department and a biodiversity consultant at the Biotope consultancy. Originally a botanist and GIS expert, I specialise in the management of innovation projects, in particular software and technological products dedicated to biodiversity.
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Island Conservation
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A conservation data scientist and field ecologist with broad interest in the application of machine learning and population genetics to the conservation of threatened species. Alex has conducted field studies throughout central and south America, the Caribbean, and North America.
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I am an IoT and embedded ML developer. I am also a nature enthusiast with keen interest in conserving the endangered species
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St. Lawrence University
Professor of Biology at St. Lawrence University
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Im an ecologist, and conservation biologist, working in Ireland at Ulster University. Im interested in using tech in applied contexts and have experience of using camera traps, songmeters, audiomoths and AI.
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Are you ready for this year's #Tech4Wildlife Photo Challenge? In anticipation, we're counting down our ten favourite entries from last year. Do you think you can top these?
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The Conservation Leadership Programme (CLP) is a training and capacity building programme that targets individuals from developing countries who are early in their conservation career and demonstrate leadership...
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The internet has a long love affair with cat pictures, but these aren’t your mom’s internet cats. Now internet cats are getting even bigger and wilder. In this article, Dr. Lisa Feldkamp talks about the work Panthera is...
26 October 2016
Caves don't tend to be well-liked ecosystems, being extremely dark, often quite cramped, and slippery. And the creepy-crawlies that live within them can be the stuff of nightmares. Nevertheless, one's attitude towards...
25 July 2016
Sharing personal 'best of' animal pictures is a favorite pastime of many camera trappers. A prolific camera trapper himself, Roland Kays has pulled together more than 600 images collected by 152 researchers from 54...
18 July 2016
Can camera traps placed in trees offer a way to rapidly inventory secretive arboreal mammals? How does this approach compare with traditional survey techniques? Dr Andy Whitworth and his colleagues set out to answer...
4 July 2016
Operating the largest tropical forest camera trap network globally, TEAM Network has accumulated over 2.6 million images. How can large datasets coupled with new techniques for data management and analysis provide...
28 April 2016
Camera traps have revolutionised wildlife research and conservation, enabling scientists to collect photographic evidence of rarely seen and often globally endangered species, with low expense, relative ease, and...
20 April 2016
When Victoria Espinel, President and CEO of BSA | The Software Alliance, spoke at the WWF Fuller Symposium, she took us on a whistle-stop tour of case studies where software and data are transforming our understanding...
10 March 2016
From artificial “sniffer” technologies to portable DNA sequencers, the Wildlife Crime Tech Challenge received hundreds of innovative ideas to help stamp out wildlife crime. Now, the Challenge is proud to announce 16...
22 January 2016
New technologies offer a lot of potential for conservation, but are there potential risks to deploying these new technologies? In this first thought piece for the Ethics of Conservation Tech Group, Dr Chris Sandbrook...
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Description | Activity | Replies | Groups | Updated |
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Hi folks, for Site - in our camera trap data base we use the mountain or National/Nature park Survey - is connected to the time scale - i.e. This mountain2015 or 2016... |
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Camera Traps | 7 years 6 months ago | |
Bringing over some of the comments we're getting on Twitter: @WILDLABSNET Our @UAlberta lab deploys ARUs at 1000s of sites in Alberta each year in... |
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Camera Traps | 7 years 6 months ago | |
We have a set of three Bushnells covering an African wild dog scent-marking site. They are at the apices of a triangle, with each camera just at the edge of the field of view of... |
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Camera Traps | 7 years 7 months ago | |
Hi Steph - just to follow up on your post: @MarcusRowcliffe , James Durrant and I have been working on a bit of software to implement the "computer vision"... |
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Camera Traps | 7 years 10 months ago | |
Hi Louise, Welcome! Unfortunately, an uncomfortably busy calendar meant I ended up missing this gathering. However, I'm sure that @SteffenOppel @... |
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Camera Traps | 7 years 10 months ago | |
We recently spoke with @andy_manu_peru about arboreal camera trapping. For anyone, for anyone who hasn't read it yet, I... |
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Camera Traps | 7 years 10 months ago | |
Some camera trap manufacturers offer solar power as an add-on or option off the shelf. I agree that using the image seems like a solution looking for a problem. |
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Camera Traps | 7 years 11 months ago | |
Hi Sorry been away, I'll list more about parts etc.. In the mean time the Pi Zero has just had an upgrade.. http://petapixel.com/2016/05/19/5-raspberry-pi-zero-... |
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Camera Traps | 8 years ago | |
This Smithsonian-Mason School of Conservation course offering looks really valuable: Camera Trapping Study Design and Data... |
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Camera Traps | 8 years 4 months ago |
Recommendations Needed: Infrared/Thermal Cameras to Install in conservation perimeter fence
17 May 2018 8:06am
Feedback Needed: Time lapse imagery
15 May 2018 11:36am
[ARCHIVED]: Camera trapping workshop, London
14 May 2018 12:38pm
Recommendations Needed: Best sensors and cameras to install along fence gaps in a conservancy
16 April 2018 10:38am
2 May 2018 1:16pm
Hello Stephanie,
Thank you for the Introduction. This is nice and I think they can be very helpful to us at this moment. Especially on the acoustic sensors and LoRa network setup initiative.
Looking forward to getting intouch with them.
Regards,
Damian
7 May 2018 11:04am
Hi Damien,
Lora technology enables a range of very efficient solutions that you can deploy over a large area. We provide consultancy and field support for deploying such networks and work with Arribada team on developing custom hardware solutions for various applications in the conservation field. I suggest you explore the existing options and let us know if you need more customized solutions.
Regards,
Luka
Institute IRNAS
Recommendations Needed: Camera for remote work using Irriduim for image upload
15 February 2018 1:02pm
9 April 2018 12:30pm
Hi swinnard,
we have built a system this year for Arribada Initiative http://blog.arribada.org/ that is working closely with ZSL that implements the functions you require to a large extent. The Arribada FMP device is open source open hardware and you can find more details here: https://github.com/IRNAS/arribada-fmp
The device has the following features:
* Solar powered
* Built-in camera
* RockBlock Iridium modem
* WiFi and LoraWAN communications (not required in you application)
* Ultrasonic distance sensor (not required in you application)
We have built a version of this device for Arribada Penguin monitoring: https://github.com/IRNAS/arribada-pmp which captures images hourly on Antarctica of penguins and stores to the SD card, without the Iridium comunication.
Note that the cost of Iridium system may be prohibitively expensive, see http://www.rock7mobile.com/products-rockblock
2000 credits for 800GBP will give about 100MB of capacity, which can translate to about one photo per day if a compressed resolution is sufficient. A full HD image will for example be 2.4MB.
A more optimal method would be to take a photo several times a day, process them using OpenCV or other image processing library to determine if the image is interesting and then send it via Iridium. This can save some data as well as generate interesting photos. All of them can be stored to the SD card and retrieved at a later point.
16 April 2018 3:26pm
Hi Institiue IRNAS,
Thank you very much for your response, it is very helpful information.
I will have a look in more detail into your camera. For us it is not possible to choose photos to be sent back, we need them all to come back via irridium, so it is definitely the part of the project that can quickly become cost prohibitive.
I've been in touch with a research group that used an Instant Detect camera from ZSL which they now want to sell, so I may well end up using that system. If not though, the system you are using looks great.
Thanks for sharing it with me.
26 April 2018 11:39pm
Dear All
Our group in Australia has developed a device for monitoring wild dogs/dingoes in remote areas and have a fully authomated system using Iridium and or 3-4G if available. This device is officially called Wild Dog Alert Node and will be capable of being deployed into remote areas, gathering image data using a unique sensor system, processing the image and sending it to the cloud where it is identified as a wild dog-dingo and an alarm is immediately triggered. In our case it is to prevent predation on livestock but this technology could easily be modified for any species. The R&D is being refined and we are expecting to start disseminating information soon in both scientific papers and general media https://invasives.com.au/research/wild-dog-alert
Conservation Technology User Guidelines Issue 4: Satellite remote sensing for conservation
23 April 2018 12:00am
Data Needed: Camera trap images for machine vision training
29 March 2017 8:06pm
29 January 2018 6:40pm
Just chiming in, and this might not be helpfull at all.. but I recently came across a blogpost about 'building the poor man's deep learning camera'. Especially as it touches https://www.makeartwithpython.com/blog/poor-mans-deep-learning-camera/
7 February 2018 3:22pm
Hey, this is really interesting @theraido, thanks for sharing. Kirk has a nice writing style that's really easy to understand.. to the point where I feel lulled into confidence that I'd be able to replicate the project without too much fuss (I suspect this might be rather a strong case of over confidence). I was also amused that waiting for the birds to appear at the feeder seemed to be the slowest part of the project.
It looks like it's going to be a series of tutorials, with the goal being to build an open platform for exploring crow intelligence. Eventually, he says like to identify individual crows in the wild, and give them the opportunity to pass a series of tests. So it'll be interesting to follow along and see whether he reaches this goal.
In case anyone is interested, the next tutorial posted so far is 'building the rich man's deep learning camera', where he shares how to build a self-contained deep learning camera to detect birds in the wild.
20 April 2018 4:19am
Sounds great!
I am doing the almost same thing, since here we have a problem between elephant and human living in the same area and conflict a lot. We would like to warn people when elephants are approching by camera trap's detection of elephants.
How is your work now? Maybe we could share some info and put it forward together.
Anton
Recommendations Needed: Software for sampling photos from video
6 July 2017 7:42pm
20 July 2017 3:01am
Thank you Jason :) I will check both the wildboo.org and the youtube video you sent. All the best
20 July 2017 3:02am
Thank you Peter...and cool picture :)
Best regards
Nuno
17 April 2018 8:23pm
My preference is to use Adobe Lightroom, allowing me to move forwards or backwards one frame at a time. I can also adjust the exposure. This is an example from one of my videos.
I hope this is helpful.
Camera Trapper from Colorado
17 April 2018 8:19pm
Camera trapping workshop, London
16 April 2018 12:00am
[ARCHIVED]: Camera-trapping data consultancy
13 April 2018 5:00pm
The Plant-Powered Camera Trap Challenge
3 April 2018 12:00am
#Tech4Wildlife Photo Challenge 2018: Our Top 10
3 March 2018 12:00am
Research: A rigorous, realistic and reproducible test of camera trap perfomance
7 November 2016 1:49pm
15 November 2016 6:08am
Also, just a general comment: some less epxensive cameras peform very well but may be more prone to 'glitches' over the duration of an extended study. I think the expectation of long term reliability is part of the reason some people choose the expenisve brand. Systematic tests of long duration reliability in field conditions would be really interesting, albeit probably too difficult/expensive to achieve.
15 November 2016 6:23am
Thank you Julia
Camera testing is certainly not Toffee's favorite activity - he would much rather be sniffing for scent marks !
The adverse effects of high temperatures on PIR are well established and they are a major problem anywhere that air temperatures get above about 30C. There is also a problem with cameras staying hotter than their surroundings for a few hours after sunset. I have also noticed that the infrared illuminator on the Reconyx actually heats up the camera.
Birds might be trickier to train than dogs, but you only need a reliable way to lure them to particular points within the field of view.
Certainly there are more factors to consider than only detection capability (though arguably that is the most important - better a fuzzy picture than none at all probably) and reliability is one of those. Bushnell Trophycams are notorious for losing their date settings (and this morning the one I am testing had done just that) for example. All sorts of equipment gets put through accelerated durability tests, and there is no reason why camera traps should not be similarly tested.
Given the huge projects that are built around camera trapping, and the scale of the conservation management decisions that are based on camera trap data it is a real problem that their performance is not tested and validated as fit for purpose.
Peter
7 February 2018 8:23pm
The sequence of images in the attached brief report shows why camera traps must be tested with real animal targets, and not with humans. The camera easily detects a human, but misses multiple images of the target dog.
Technology Empowered Conservation Lecture Series
18 January 2018 12:00am
Instant Detect 2.0: A Connected Future for Conservation
17 January 2018 12:00am
Resource: Camelot - new camera trap software
1 November 2016 11:16am
18 December 2017 12:16pm
Hi Egil, I think Camelot has a reasonable story around most of these things. Here's how I see it:
I set up a survey and I add the information about all the camera trapping stations (including camera IDs).
I import the photos. The database reads the data, time and camera ID from the photos so photos are linked to a camera trap location.
Camelot has two modes for importing data: a bulk import, and a per-session import. It sounds like you have many images up-front, and so bulk import may be the way to go. In this case Camelot can create the camera trap stations based on the location of the images, and their metadata.
http://camelot-project.readthedocs.io/en/latest/bulkimport.html
Then I do a quick first pass where I assign a species (tiger, leopard, etc) to each photo.
Yes, I expect you'll find the library UI suitable for this.
In a second round I want to identify the tigers. So I get all photos labelled with 'tiger'. Obviously the first one is a new animal thus I want a 'button' which allows me to add a new animal. Then in following pictures there is a drop-down menu with identified tigers. The order of the pictures presented is by camera trap location, data, time and then the next nearest camera trap location.
The sighting added in the first pass can be edited to add-in the individual, and new individuals can be added straight from the dropdown menu. Camelot does not have the ability to define a custom ordering of images: the ordering is always by camera trap station, by capture time (broadly; it's a bit more nuanced than this in reality). However does have the notion of "reference images", where images flagged in this way can be displayed, in another window (which can handy where multiple monitors are available), based on the currently selected sighting field data.
If things go as planned I'll likely be running into 100,000's of pictures, so I need to be able to do the first round pretty quickly. Then there will be 4-5 species I need to identify at the level of the individual. There will probably up to about 200 individuals of a species, but never more than about 20 at each camera trapping station, and the vast majority of individuals won't show up at more than 4-5 camera trapping stations either.
Camelot does support this sort of raw scale (x00,000 images) and has a reasonably efficient UI for identification. The information here may be relevant, depending on how many multiples of 100,000 it turns out to be.
However Camelot does not currently offer the ability to limit individuals in a field depending on the selected species (it is always the same individuals available in a dropdown regardless of the selected species). Potentially this limation could be worked around by having a dropdown available for each species, which will at least highlight in an export where a data-entry error may have arisen. (e.g., individual chosed in the "individuals" for species X field, but the selected species is Y.)
At 200 individuals the workflow for reference images could start to break down too, depending on the level of familiarity with the individuals. (i.e., repeatedly searching through all reference-quality images of a species, or images for a couple dozen individuals to make an identification could be onerous.)
I've looked around and I think Camelot is closest to what I want, but I wonder if I could get it really close to what I want. I have quite a bit of experience with SQL and building access databases, but I thought, with the prevalence of camera traps these days, that there would be more packages out there.
I agree Camelot really only goes part-way to meeting the requirements. It should be workable to use Camelot for the purpose, though support for individuals is relatively new and really hasn't been optimised for this sort of scale. It seems like the two places where Camelot is most lacking for this workflow are (and correct me if I'm wrong):
- lack of some more flexible ordering system for reference images against the selected image(s)
- lack of ability to filter options for a single "individuals" field based on the selected species
It'll likely be some time before these features are available in Camelot, but I'll add them to the development backlog.
-Chris
18 December 2017 4:21pm
Thanks for the reply Chris!
When you state:
"Camelot has two modes for importing data: a bulk import, and a per-session import. It sounds like you have many images up-front, and so bulk import may be the way to go. In this case Camelot can create the camera trap stations based on the location of the images, and their metadata."
When you mention 'location' you refer to the location of the images from where they are imported from? And not the gps location from the EXIF data if it's available, right? In my case no gps data would be available. But if I've downloaded the images to a hard drive in the field, and then back in the office import them into Camelot I can tell Camelot that the pictures from folder X belong to camera station 1?
You're right about the two points for optimizing. If photos can be ordered by station, date, time, and reference images ordered by 'distance to station', it greatly reduces the number of reference images to look through as the vast majority of individuals will be recorded at only a few stations, effectively trimming down the number of individuals to look through from 100's to a few dozen.
Cascading the drop down lists Species > individuals (or even area > species > individuals, or species > area > individuals, or in some cases species > group > individuals) would be beneficial too in the process of identifying individuals. In a SQL database this isn't hard to code into the fields on a form, but might not be that easy in Camelot as you would have to chose if the field you add depends on another field on the form.
19 December 2017 8:10am
Hi Egil,
When you mention 'location' you refer to the location of the images from where they are imported from? And not the gps location from the EXIF data if it's available, right?
Yes, that's right -- I should have said the 'directory of those images'.
But if I've downloaded the images to a hard drive in the field, and then back in the office import them into Camelot I can tell Camelot that the pictures from folder X belong to camera station 1?
Yes, that's right. Images can be dragged & dropped into existing camera trap stations, which have GPS coordinates associated. For the bulk import case, the data scanned from the image would need to be joined with GPS data to produce the final CSV for upload.
And thanks for the confirmation on how you'd expect that functionality should behave.
-Chris
FIT Cheetahs
4 December 2017 12:00am
HWC Tech Challenge Update: Meet the Judges
20 October 2017 12:00am
Research: Trail Camera Comparison Testing (results)
3 October 2017 12:53pm
19 October 2017 11:14am
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Best Practices: Camera trap survey guide released
12 October 2017 12:17pm
Recommendations Needed: Best camera traps for Central African rainforests?
10 October 2017 4:13pm
12 October 2017 11:34am
Hi John,
They will want a camera with good tolerance to humidity + precipitation (so a camera with a proper O-ring seal, and pack it with regularly-dried silica gel). Elephants will likely have a go at the cameras - so they will want to think about protecting their cameras with heavy-duty steel security boxes (perhaps with welded on spikes, which has worked in Thailand) and minimising smell left on and around their cameras during setup (e.g. use gloves, don't smoke or leave food).
Apart from that, the usual considerations apply: good detection circuitry (less of a problem if targeting elephants though, as they are massive) and battery life always helps.
Laila Bahaa-el-din et al. used Panthera and Scoutguard in Gabon; the Goualougo Triangle Ape Project in Rep Congo uses Reconyx I think; Julia Gessner et al. used Reconyx in Rep. Congo and Cameroon. The latter reported that (of 47 cameras), 4 were taken out by elephants and one by a leopard!
Ollie
Download New Conservation Tech Guidelines: Camera Traps, Acoustics and LiDAR
11 October 2017 12:00am
How to lose a BRUV in 10 days
26 September 2017 12:00am
Survey: Camera Trap Survey with WWF-UK
20 September 2017 9:48pm
Recommendations Needed: GSM Camera Traps
24 May 2017 1:42pm
10 July 2017 1:24pm
Hi Chloe,
I've had promising results with the Scoutguard MG983G-30M, and I believe a 4G version has just been released. The 3G version has some useful features, like two-way communication to change settings/get images and a audio call feature (which I haven't used). I'd be very pleased to hear how your tests go.
Cheers,
Rob
10 July 2017 4:04pm
Thanks everyone,
@TopBloke @Kai - I've not come across the Ltl Acorn ones - will take a look. So does that mean if it was purchased in the UK it would be locked to a UK SIM then? That might be problemativ as we tend to purchase here to test and set up before sending out when staff are heading back to the country of deployment.
@Rob+Appleby - That's good to know about the Scoutguard MG983G-30M too, thanks.
A supplier is going to send me a new model by the same people who make Scoutguard to test but he hasn't said what it is. Maybe it's the 4G one. Whatever it is if it is any good I will feed back on here
Chloe
10 July 2017 4:42pm
@@Chloe+Aust - pls google “ltl acorn uk”, you will get a lot info. No, no limited, just the company's sale rules. You could use it anywhere, but for example, if you wants to use it in kenya, you need a local sim card, such as safaricom... when you check photos from china, you will find most of them photoed by are ITI acorn. Panda,tiger, snow leopord, cloud leopard, leopard, golden cat....
Scoutguard is also a good china camrea traps. oldest company. only company have espano menu. but they are focus on US hunting market. not special for wildlife researching use.
The LOREDA is also good, the only brand focus on wildlife-bio researching camra traps in china. Might be do not have dealers/stock in uk.
From the Field: Developing a new camera trap data management tool
7 July 2017 12:00am
Article: Google's cloud vision for automated identification of camera trap photos
12 April 2016 1:04pm
7 August 2016 11:25pm
An update to the automated species identification debate:
A paper has recently come out which used deep learning ("very deep convolutional networks") and managed 89% accuracy for the Snapshot Serengeti Zooniverse dataset, IF the image was first manually cropped around the animal. Seriously, who has time to do that? If the image remained uncropped they managed a woeful 35% accuracy.
Perhaps we have a long wait ahead of us for this to become a practical reality?
22 June 2017 7:22am
Note that paper isn't actually a journal article, hasn't gone through peer review, its a preprint.
24 June 2017 12:50am
Fair point, it isn't a peer reviewed article as yet. I had a poke around out of curiosity and wasn't able to track down a fully published paper yet (thought it's been a while since this preprint was released...). On reading, were there any issues that stood out to you that others should be aware of? And, as Ollie pointed out, the research wasn't getting a great response rate from uncropped images - I wonder if perhaps, given the time since the original publication, the results may have actually improved by now? It seems (from my interested but unqualified observer perspective) that the field is moving forward in leaps and bounds, such that a paper pre-published in march 2016 may very well be out of date by this stage...
Resources: Panthera Camera Trap information
5 April 2017 8:47pm
26 May 2017 12:39am
My understanding is that the Panthera cameras are only available to people or groups in Panthera's network. I tried to get some a few years back and had no luck, even for a project that had received Pathera funding in the past.
Regarding the poacher cam, I know @ColbyLoucks at WWF has developed a system using infrared thermal cameras that worked in the field trials.
Putting on my black thinking hat, the design as shown of the poacher cam will not be effective long-term once poachers know to look for it. Eric Dinerstein had worked on a project where such cameras would be concealed in a vine or some other organic-looking encasement.
26 May 2017 12:04pm
black thinking hat ! Interesting and honest.
Machine learning, meet the ocean
10 May 2017 12:00am
2 May 2018 12:27pm
Hi Damian,
This may be part of your conversation with Alasdair and the ZSL team already, but you might want to connect with @Institute+IRNAS as well. They're collaborating with Alasdair's Arribada Initative, ZSL and others to tackle some of the challenges you've also been looking into (e.g LoRA systems). There's some more info about their work here: http://irnas.eu/conservation
Luka (if it is indeed you who is manning the Institute IRNAS member profile here on wildlabs), maybe you can jump in here?
Steph