# Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar

arXiv: https://arxiv.org/abs/1912.06613 · HTML: https://mlsanity.com/paper/1912.06613/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2019.

## Built on by (most cited first, 30 of 40)

- [Raw High-Definition Radar for Multi-Task Learning](https://mlsanity.com/paper/2112.10646/) (2021)
- [RadarScenes: A Real-World Radar Point Cloud Data Set for Automotive Applications](https://mlsanity.com/paper/2104.02493/) (2021)
- [Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2304.10410/) (2023)
- [Radar-Camera Pixel Depth Association for Depth Completion](https://mlsanity.com/paper/2106.02778/) (2021)
- [RODNet: A Real-Time Radar Object Detection Network Cross-Supervised by Camera-Radar Fused Object 3D Localization](https://mlsanity.com/paper/2102.05150/) (2021)
- [WaterScenes: A Multi-Task 4D Radar-Camera Fusion Dataset and Benchmarks for Autonomous Driving on Water Surfaces](https://mlsanity.com/paper/2307.06505/) (2023)
- [Multi-View Radar Semantic Segmentation](https://mlsanity.com/paper/2103.16214/) (2021)
- [Exploring Radar Data Representations in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2312.04861/) (2023)
- [DART: Implicit Doppler Tomography for Radar Novel View Synthesis](https://mlsanity.com/paper/2403.03896/) (2024)
- [DPFT: Dual Perspective Fusion Transformer for Camera-Radar-based Object Detection](https://mlsanity.com/paper/2404.03015/) (2024)
- [Radar Fields: Frequency-Space Neural Scene Representations for FMCW Radar](https://mlsanity.com/paper/2405.04662/) (2024)
- [Physics to the Rescue: Deep Non-line-of-sight Reconstruction for High-speed Imaging](https://mlsanity.com/paper/2205.01679/) (2022)
- [Gaussian Radar Transformer for Semantic Segmentation in Noisy Radar Data](https://mlsanity.com/paper/2212.03690/) (2022)
- [What You Can Learn by Staring at a Blank Wall](https://mlsanity.com/paper/2108.13027/) (2021)
- [Radar and Camera Fusion for Object Detection and Tracking: A Comprehensive Survey](https://mlsanity.com/paper/2410.19872/) (2024)
- [Recent Advances in Embedding Methods for Multi-Object Tracking: A Survey](https://mlsanity.com/paper/2205.10766/) (2022)
- [Few-shot Non-line-of-sight Imaging with Signal-surface Collaborative Regularization](https://mlsanity.com/paper/2211.15367/) (2022)
- [Tackling Clutter in Radar Data -- Label Generation and Detection Using PointNet++](https://mlsanity.com/paper/2303.09530/) (2023)
- [Fast Rule-Based Clutter Detection in Automotive Radar Data](https://mlsanity.com/paper/2108.12224/) (2021)
- [Role of Transients in Two-Bounce Non-Line-of-Sight Imaging](https://mlsanity.com/paper/2304.01308/) (2023)
- [The Radar Ghost Dataset -- An Evaluation of Ghost Objects in Automotive Radar Data](https://mlsanity.com/paper/2404.01437/) (2024)
- [R4D: Utilizing Reference Objects for Long-Range Distance Estimation](https://mlsanity.com/paper/2206.04831/) (2022)
- [Virtual Mirrors: Non-Line-of-Sight Imaging Beyond the Third Bounce](https://mlsanity.com/paper/2307.14341/) (2023)
- [Occlusion Fields: An Implicit Representation for Non-Line-of-Sight Surface Reconstruction](https://mlsanity.com/paper/2203.08657/) (2022)
- [Curvature regularization for Non-line-of-sight Imaging from Under-sampled Data](https://mlsanity.com/paper/2301.00406/) (2023)
- [Generalizable Non-Line-of-Sight Imaging with Learnable Physical Priors](https://mlsanity.com/paper/2409.14011/) (2024)
- [Tracking without Seeing: Geospatial Inference using Encrypted Traffic from Distributed Nodes](https://mlsanity.com/paper/2603.27811/) (2026)
- [Deep learning for radar data exploitation of autonomous vehicle](https://mlsanity.com/paper/2203.08038/) (2022)
- [Teacher-Student Knowledge Distillation for Radar Perception on Embedded Accelerators](https://mlsanity.com/paper/2303.07586/) (2023)
- [G3R: Generating Rich and Fine-grained mmWave Radar Data from 2D Videos for Generalized Gesture Recognition](https://mlsanity.com/paper/2404.14934/) (2024)

## Abstract (arXiv)

Conventional sensor systems record information about directly visible objects, whereas occluded scene components are considered lost in the measurement process. Non-line-of-sight (NLOS) methods try to recover such hidden objects from their indirect reflections - faint signal components, traditionally treated as measurement noise. Existing NLOS approaches struggle to record these low-signal components outside the lab, and do not scale to large-scale outdoor scenes and high-speed motion, typical in automotive scenarios. In particular, optical NLOS capture is fundamentally limited by the quartic intensity falloff of diffuse indirect reflections. In this work, we depart from visible-wavelength approaches and demonstrate detection, classification, and tracking of hidden objects in large-scale dynamic environments using Doppler radars that can be manufactured at low-cost in series production. To untangle noisy indirect and direct reflections, we learn from temporal sequences of Doppler velocity and position measurements, which we fuse in a joint NLOS detection and tracking network over time. We validate the approach on in-the-wild automotive scenes, including sequences of parked cars or house facades as relay surfaces, and demonstrate low-cost, real-time NLOS in dynamic automotive environments.