PathFinder: Attention-Driven Dynamic Non-Line-of-Sight Tracking with a Mobile Robot
<|reference_start|>PathFinder: Attention-Driven Dynamic Non-Line-of-Sight Tracking with a Mobile Robot: 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.<|reference_end|>
@article{kannapiran2024pathfinder:,
    title={PathFinder: Attention-Driven Dynamic Non-Line-of-Sight Tracking with a
  Mobile Robot},
    author={Shenbagaraj Kannapiran, Sreenithy Chandran, Suren Jayasuriya and
  Spring Berman},
    journal={arXiv preprint arXiv:2404.05024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.05024},
    primaryClass={cs.CV cs.RO}
}