ClassifyMe: A Field-Scouting Software for the Identification of Wildlife in Camera Trap Images

Area of Science:

• Ecology
• Computer Vision
• Wildlife Monitoring

Background:

• Ecological field research relies heavily on camera traps for wildlife monitoring.
• Manual analysis of large camera trap image datasets is time-consuming and labor-intensive.
• Existing automated solutions often require internet connectivity and cloud processing, posing cost and privacy concerns.

Purpose of the Study:

• To introduce ClassifyMe, a novel software tool for automated animal species identification from camera trap images.
• To provide ecologists with an efficient, user-friendly, and locally operating solution for image analysis.
• To enhance the accessibility and cost-effectiveness of wildlife monitoring through advanced computer vision.

Main Methods:

• Development of a software tool, ClassifyMe, featuring a pre-trained model for species identification.
• Implementation of automated image sorting into species-specific folders and filtering of false triggers.
• Ensuring the software operates entirely on the user's local machine without requiring an internet connection.

Main Results:

• ClassifyMe successfully automates the identification of animal species and other objects from camera trap images.
• The software provides a report file detailing species detections and automatically sorts images.
• False triggers are effectively filtered and sorted, streamlining data processing.

Conclusions:

• ClassifyMe offers a state-of-the-art, on-device computer vision solution for camera trap data analysis.
• The local processing capability ensures data control, privacy, and cost savings for users.
• This tool empowers ecologists with efficient in-situ wildlife monitoring capabilities.