The features described on this page require a Camelot Account.
Typically most images captured by motion-triggered cameras are do not have valuable content, as the motion sensor was triggered by causes other than some nearby animal, such as a gust of wind. The result is that much time tends to be spent sifting through images to find those that actually have interesting content.
To help you focus on the images that matter most, Camelot offers animal detection. In practice, this enables two features:
- the ability to filter images in the library based on whether they have animals or not, even before they have been processed
- bounding boxes around the detected animals
Animal detection is provided via an online service. As such, its use requires an adequate internet connection and it is only available after registration.
For most users, set up is a simple matter of following the prompts in the administration interface:
Once login has succeeded, save the configuration, restart Camelot, and the animal detection should now start to upload & process the available images.
How it works¶
Once set up, the animal detection automatically starts, and will continue behind-the-scenes. You can see a summary of this detection activity at any time via the top “Detection” menu:
Detection is performed by following the steps described below.
The detection starts by retrieving the cameras for each session of all trap stations. A batch of images is formed for each of these cameras, which is referred to as a ‘image batch creation’.
Images will be uploaded to secure cloud storage location dedicated to this image batch. When all images have been uploaded, this batch is submitted for processing.
Camelot has partnered with Microsoft’s AI for Earth team, who at this stage, perform the image processing using their machine learning models. Processing can take some time, typically between several minutes and several hours, depending on the number of images in the batch. Camelot will periodically check for new results for the submitted batches.
Once processed, the suggestions offered by the image processing are created in Camelot. “High confidence” suggestions are reflected in the library in the image filtering, and with bounding boxes.
Whether a suggestion is considered “high-confidence” is determined by the
“Confidence threshold” setting in the Administration UI, which is a number
1 indicating the likelihood of the suggestion being correct.
Suggestions made at or beyond this level of confidence are “high-confidence”.
The threshold can be changed at any time. All suggestions are stored by Camelot regardless of the confidence threshold, so changes to the threshold apply retrospectively.
Suggestions created by animal detection include whether the an
person was detected. The “Has animal?” filter in the Library shows only
those images that have a high-confidence
animal suggestion, or has an
Images with high-confidence suggestions that they contain people can be shown using the filter:
The current detector status is reflected to the right of the “Activity” section. The detector can be paused, running or in an offline status.
The detector can be paused at any time. This can be particularly useful for slower connections, where the network bandwidth required to upload images may noticably degrade the connection. In this event, the detector can be paused which will prevent any new uploads taking place until it is resumed again.
While paused, the detector will take no further action, including creating suggestions from any newly-processed images. All such actions will be queued until the detector is resumed again.
When toggling between running and paused it may take several seconds before Camelot reflects the new status. This is normal: the status is only reflected once it is actually processed, which means the current activities (e.g., upload of the current image) will need to complete before the new status is in effect.
Camelot may signal here that the authentication failed if the configured username and password are rejected. In this event, the detector is effectively offline and Camelot will need to be restarted before the detector will attempt to recheck the credentials and run again.
What we would not want is for the internet to go down for an hour or two, and find a large number of batches and image uploads have failed as a result.
In the event Camelot cannot access the online services it needs, it will pause the processing automatically. Once the connection is restored, processing will be automatically resumed.
If the system is paused through the user interface, Camelot will respect this even if the internet connection cuts out and comes back. Camelot will always pause the animal detection system in the event it cannot communicate with the systems it needs to.
The rerun button (introduced in Camelot 1.6.8) allows for rerunning failed batches. Any batch which has previously failed will be considered anew for animal detection and attempt to run immediately.
Use of the rerun failed batches this will not reset any statistics for failed batches.
Camelot tracks and aggregates all animal detection activity, presenting it on the activity page described above. This gives an overview of what is happening within Camelot, and provides visibility in to any errors which may be occurring.
This page reports failures and suspended tasks. A failure is a step which cannot be completed, whereas a a step which has been suspend will be retried again after other batches have been processed.
Failures and suspensions happen for a variety of reasons, including network disruptions or delays from processing particularly large batches. Some errors may mean that suggestions for a small number of images are not created where they otherwise could have been, though typically these are not worth worrying about; false negatives can be assumed to exist in the suggestions anyway, and thus you should treat failures as potential false-negative.