Introduction
Camera-trap image classifiers are specialised software tools designed to automatically identify and categorise wildlife captured by camera traps. They are invaluable for wildlife monitoring and research, but they often have limitations, particularly regarding species coverage. The accuracy and reliability of classification largely depend on the availability of training data. Many classifiers can only accurately identify species that are well-represented in their training datasets. Less common or newly observed species may not be recognised accurately, highlighting the need for ongoing data collection and model updates.
Considerations for Making a Balanced Choice
Species Recognition: Does the software recognise the specific species you are monitoring?
Counting Individuals: Can it accurately count the number of individuals in images?
Video Processing: Can it handle video footage? Does it prevent double-counting the same individuals lingering in front of the camera?
Identification of Named Individuals: Can it identify and distinguish named or known animals?
Online vs Offline Usage: Do you require a tool that works online, offline, or both?
AI Training Dataset Creation: Can the software generate image lists for further AI training?
Automatic Statistics: What statistics are generated automatically, and which are supported by the tool?
User-Friendliness: How easy is it to learn and operate the software?
Processing Efficiency: How long does it take to process large batches of images, e.g., 1,000 or more?
Human-in-the-Loop: Does the system allow human intervention to validate or correct results?
Support and Assistance: Do the providers offer hands-on support or user guidance?
Cost: What are the costs, including licensing, subscriptions, or additional fees?
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