  The study of non-line-of-sight (NLOS) imaging is growing due to its many
potential applications, including rescue operations and pedestrian detection by
self-driving cars. However, implementing NLOS imaging on a moving camera
remains an open area of research. Existing NLOS imaging methods rely on
time-resolved detectors and laser configurations that require precise optical
alignment, making it difficult to deploy them in dynamic environments. This
work proposes a data-driven approach to NLOS imaging, PathFinder, that can be
used with a standard RGB camera mounted on a small, power-constrained mobile
robot, such as an aerial drone. Our experimental pipeline is designed to
accurately estimate the 2D trajectory of a person who moves in a
Manhattan-world environment while remaining hidden from the camera's
field-of-view. We introduce a novel approach to process a sequence of dynamic
successive frames in a line-of-sight (LOS) video using an attention-based
neural network that performs inference in real-time. The method also includes a
preprocessing selection metric that analyzes images from a moving camera which
contain multiple vertical planar surfaces, such as walls and building facades,
and extracts planes that return maximum NLOS information. We validate the
approach on in-the-wild scenes using a drone for video capture, thus
demonstrating low-cost NLOS imaging in dynamic capture environments.
