Embedded Imagers: Detecting, Localizing, and Recognizing Objects and Events in Natural Habitats

Imaging sensors, or “imagers,” embedded in the natural environment enable remote collection of large quantities of data, thus easing the design and deployment of sensing systems in a variety of application domains. Yet, the data collected from such imagers are difficult to interpret due to a variety of “nuisance factors” in the data formation process, such as illumination, vantage point, partial occlusions, etc. These are especially severe in natural environments, where the objects of interest (e.g., plants, animals) have evolved to blend with their habitat, exhibit complex variability in shape and appearance, and perform rapid motions against dynamic backgrounds with rapid illumination changes. We describe three applications that exemplify these problems and the solutions we developed. First, we show how temporal oversampling can simplify the analysis of a slow process such as the avian nesting cycle. Then, we show how to overcome temporal undersampling in order to detect birds at a feeder station. Finally, we show how to exploit temporal consistency to reliably detect pollinators as they visit flowers in the field.