HOOD: Real-Time Human Presence and Out-of-Distribution Detection Using FMCW Radar

• URL: http://arxiv.org/abs/2308.02396v2
• Date: Tue, 26 Mar 2024 23:17:24 GMT
• Title: HOOD: Real-Time Human Presence and Out-of-Distribution Detection Using FMCW Radar
• Authors: Sabri Mustafa Kahya, Muhammet Sami Yavuz, Eckehard Steinbach,
• Abstract summary: Real-time capable human presence and out-of-distribution (OOD) detection method using 60 GHz short-range FMCW radar. Hood aims to accurately detect the presence of humans in the presence or absence of moving and stationary disturbers. On our dataset collected with a 60 GHz short-range FMCW radar, we achieve an average AUROC of 94.36%.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detecting human presence indoors with millimeter-wave frequency-modulated continuous-wave (FMCW) radar faces challenges from both moving and stationary clutter. This work proposes a robust and real-time capable human presence and out-of-distribution (OOD) detection method using 60 GHz short-range FMCW radar. HOOD solves the human presence and OOD detection problems simultaneously in a single pipeline. Our solution relies on a reconstruction-based architecture and works with radar macro and micro range-Doppler images (RDIs). HOOD aims to accurately detect the presence of humans in the presence or absence of moving and stationary disturbers. Since HOOD is also an OOD detector, it aims to detect moving or stationary clutters as OOD in humans' absence and predicts the current scene's output as "no presence." HOOD performs well in diverse scenarios, demonstrating its effectiveness across different human activities and situations. On our dataset collected with a 60 GHz short-range FMCW radar, we achieve an average AUROC of 94.36%. Additionally, our extensive evaluations and experiments demonstrate that HOOD outperforms state-of-the-art (SOTA) OOD detection methods in terms of common OOD detection metrics. Importantly, HOOD also perfectly fits on Raspberry Pi 3B+ with an ARM Cortex-A53 CPU, which showcases its versatility across different hardware environments. Videos of our human presence detection experiments are available at: https://muskahya.github.io/HOOD

Related papers

• Compressing VAE-Based Out-of-Distribution Detectors for Embedded Deployment [2.27626288527213]
  Out-of-distribution detectors can act as safety monitors in embedded cyber-physical systems. Deep neural networks make it difficult to meet real-time deadlines on embedded systems with memory and power constraints. We propose a design methodology that combines all three compression techniques and yields a significant decrease in memory and execution time.
  arXiv  Detail & Related papers  (2024-09-02T00:39:29Z)
• FOOD: Facial Authentication and Out-of-Distribution Detection with Short-Range FMCW Radar [0.0]
  This paper proposes a short-range FMCW radar-based facial authentication and out-of-distribution (OOD) detection framework. Our pipeline jointly estimates the correct classes for the in-distribution (ID) samples and detects the OOD samples to prevent their inaccurate prediction. For our dataset, gathered using a 60 GHz short-range FMCW radar, our network achieves an average classification accuracy of 98.07% in identifying in-distribution human faces.
  arXiv  Detail & Related papers  (2024-06-06T23:08:03Z)
• Radar Fields: Frequency-Space Neural Scene Representations for FMCW Radar [62.51065633674272]
  We introduce Radar Fields - a neural scene reconstruction method designed for active radar imagers. Our approach unites an explicit, physics-informed sensor model with an implicit neural geometry and reflectance model to directly synthesize raw radar measurements. We validate the effectiveness of the method across diverse outdoor scenarios, including urban scenes with dense vehicles and infrastructure.
  arXiv  Detail & Related papers  (2024-05-07T20:44:48Z)
• Multi-stage Learning for Radar Pulse Activity Segmentation [51.781832424705094]
  Radio signal recognition is a crucial function in electronic warfare. Precise identification and localisation of radar pulse activities are required by electronic warfare systems. Deep learning-based radar pulse activity recognition methods have remained largely underexplored.
  arXiv  Detail & Related papers  (2023-12-15T01:56:27Z)
• HAROOD: Human Activity Classification and Out-of-Distribution Detection with Short-Range FMCW Radar [0.0]
  We propose HAROOD as a short-range FMCW radar-based human activity classifier and out-of-distribution detector. It aims to classify human sitting, standing, and walking activities and to detect any other moving or stationary object as OOD. On our dataset collected by 60 GHz short-range FMCW radar, we achieve an average classification accuracy of 96.51%.
  arXiv  Detail & Related papers  (2023-12-14T12:56:28Z)
• MCROOD: Multi-Class Radar Out-Of-Distribution Detection [0.0]
  This work proposes a reconstruction-based multi-class OOD detector that operates on radar range doppler images (RDIs) The detector aims to classify any moving object other than a person sitting, standing, or walking as OOD. We also provide a simple yet effective pre-processing technique to detect minor human body movements like breathing.
  arXiv  Detail & Related papers  (2023-03-10T22:44:24Z)
• Reconstruction-based Out-of-Distribution Detection for Short-Range FMCW Radar [0.0]
  We propose a novel reconstruction-based OOD detector to operate on the radar domain. Our method exploits an autoencoder (AE) and its latent representation to detect the OOD samples. We achieve an AUROC of 90.72% on our dataset collected by using 60 GHz short-range FMCW Radar.
  arXiv  Detail & Related papers  (2023-02-27T23:03:51Z)
• Unsupervised Evaluation of Out-of-distribution Detection: A Data-centric Perspective [55.45202687256175]
  Out-of-distribution (OOD) detection methods assume that they have test ground truths, i.e., whether individual test samples are in-distribution (IND) or OOD. In this paper, we are the first to introduce the unsupervised evaluation problem in OOD detection. We propose three methods to compute Gscore as an unsupervised indicator of OOD detection performance.
  arXiv  Detail & Related papers  (2023-02-16T13:34:35Z)
• Rethinking Out-of-distribution (OOD) Detection: Masked Image Modeling is All You Need [52.88953913542445]
  We find surprisingly that simply using reconstruction-based methods could boost the performance of OOD detection significantly. We take Masked Image Modeling as a pretext task for our OOD detection framework (MOOD)
  arXiv  Detail & Related papers  (2023-02-06T08:24:41Z)
• ATOM: Robustifying Out-of-distribution Detection Using Outlier Mining [51.19164318924997]
  Adrial Training with informative Outlier Mining improves robustness of OOD detection. ATOM achieves state-of-the-art performance under a broad family of classic and adversarial OOD evaluation tasks.
  arXiv  Detail & Related papers  (2020-06-26T20:58:05Z)
• Robust Out-of-distribution Detection for Neural Networks [51.19164318924997]
  We show that existing detection mechanisms can be extremely brittle when evaluating on in-distribution and OOD inputs. We propose an effective algorithm called ALOE, which performs robust training by exposing the model to both adversarially crafted inlier and outlier examples.
  arXiv  Detail & Related papers  (2020-03-21T17:46:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.