A model-agnostic active learning approach for animal detection from camera traps
This addresses the challenge of excessive labeling costs for wildlife monitoring and conservation, offering an incremental improvement by making active learning applicable without full model access.
The paper tackles the problem of reducing labeling effort for animal detection from camera traps by proposing a model-agnostic active learning approach that integrates uncertainty and diversity at object and image levels, achieving state-of-the-art detector performance with only 30% of the training data.
Smart data selection is becoming increasingly important in data-driven machine learning. Active learning offers a promising solution by allowing machine learning models to be effectively trained with optimal data including the most informative samples from large datasets. Wildlife data captured by camera traps are excessive in volume, requiring tremendous effort in data labelling and animal detection models training. Therefore, applying active learning to optimise the amount of labelled data would be a great aid in enabling automated wildlife monitoring and conservation. However, existing active learning techniques require that a machine learning model (i.e., an object detector) be fully accessible, limiting the applicability of the techniques. In this paper, we propose a model-agnostic active learning approach for detection of animals captured by camera traps. Our approach integrates uncertainty and diversity quantities of samples at both the object-based and image-based levels into the active learning sample selection process. We validate our approach in a benchmark animal dataset. Experimental results demonstrate that, using only 30% of the training data selected by our approach, a state-of-the-art animal detector can achieve a performance of equal or greater than that with the use of the complete training dataset.