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Understanding External Influences on Target Detection and Classification Using Camera Trap Images and Machine
Sally O A Westworth1, Carl Chalmers2, Paul Fergus2
1School of Biological and Environmental Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK.
Sensors (Basel, Switzerland)
|July 27, 2022
Summary
Machine learning (ML) shows potential for camera trap (CT) image analysis, but human analysts currently outperform ML in wildlife detection and classification. Factors like distance and vegetation significantly impact accuracy for both methods.
Area of Science:
- Ecology
- Computer Science
- Conservation Biology
Background:
- Automating camera trap (CT) image processing with machine learning (ML) is crucial for time-sensitive conservation efforts.
- Factors influencing the accuracy of ML-based CT image analysis remain largely uninvestigated.
Purpose of the Study:
- To evaluate the impact of various environmental and subject-related factors on wildlife and human detection and classification in CT images.
- To compare the performance of human analysts versus ML algorithms in processing CT images.
- To identify key challenges and potential improvements for ML-based CT image analysis.
Main Methods:
- Analysis of CT images from western Tanzania, including wildlife data from existing datasets and human data from experimental setups.
- Evaluation of both human analyst and ML approaches at the detection and classification levels.
- Assessment of factors such as occlusion, distance, vegetation, size, height, orientation, time of day, and color.
Main Results:
- Distance and occlusion, along with dense vegetation, significantly affected detection probability (DP) and correct classification (CC).
- Human analysts achieved higher DP (81.1%) and CC (76.6%) for wildlife compared to ML (41.1% DP, 47.5% CC).
- Both methods showed comparable performance in detecting humans during daylight and dusk.
Conclusions:
- Human analysts currently provide superior accuracy for wildlife detection and classification in CT images over ML.
- Environmental factors like distance and vegetation density are critical limitations for current ML-based CT image processing.
- Adherence to recommendations is expected to improve ML performance, making it a valuable tool for biodiversity conservation and threat monitoring.
