PathFinder: Attention-Driven Dynamic Non-Line-of-Sight Tracking with a Mobile Robot

• URL: http://arxiv.org/abs/2404.05024v1
• Date: Sun, 7 Apr 2024 17:31:53 GMT
• Title: PathFinder: Attention-Driven Dynamic Non-Line-of-Sight Tracking with a Mobile Robot
• Authors: Shenbagaraj Kannapiran, Sreenithy Chandran, Suren Jayasuriya, Spring Berman,
• Abstract summary: 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. 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.
• Score: 3.387892563308912
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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