Currently, WildTrax has not encountered a make and model of camera that it does not support. However, it cannot populate data from fields that do not exist in the image Exif data. Metadata fields are stored in the image metadata e.g., “Sequence” or “TriggerMode”; however, the fields stored as image metadata or “Exif” data vary by camera make and model. If it seems like you are having issues due to the make or model of your camera, please contact firstname.lastname@example.org. You can also refer to Phil Harvey’s ExifTool Tag Names to determine which tags are available for your camera make and model.
If you are purchasing remote cameras for the first time, Reconyx cameras (e.g., HF2, HF2X, PC800, PC900, and HC600 models) are greater for first-time users as they are user-friendly and intuitive.
High-quality SandDisk SD cards or Kingstone Class 4 and 10 SD cards are frequently used. We would not recommend anything below a Class 4 write speed.
For more information on camera brands, please consult the Remote Camera Survey Guidelines.
WildTrax integrates the following:
(1) Microsoft’s Megadetector v5, that automatically tags images of vehicles, animals, humans or NONE.
(2) Microsoft’s MegaClassifer v0.1.
(3) A “Staff/setup” tagger designed to filter out images of humans at the camera’s deployment and retrieval. When enabled in your project’s settings, these tools will automatically filter and tag your images.
This results in less time spent sifting through false fires and more time spent focusing on the species you want to tag.
Cameras can sometimes capture images that do not contain wildlife—‘false fires’—due to movement in vegetation or changes in sunlight. These false fires can increase processing cost and time. To aid in processing these images, WildTrax contains a model to automatically identify false fires, allowing them to be removed before further processing. The model uses training data from 1,325 camera deployments as well as a trained network, CaffeNet, specifically modified for WildTrax. This tool results in less human time spent sifting through images of vegetation movement. The model was validated with an additional 121 camera deployments with 79,451 false-fire images. The model identified 34,456 (43.6%) of false fires with a 0.2% error (false positive) rate. That is, more than 40% of false fires can be reliably (0.2% error) removed before processing. Depending on the camera unit used, image quality and habitat type results may vary.
Images cannot be deleted individually in WildTrax (including those of humans); instead, WildTrax allows you to filter images of humans using the results from Megadetector (if enabled in project settings) as well as select options to opt-in for human blurring (can be enabled in organization settings).