# MCROOD: Multi-Class Radar Out-Of-Distribution Detection

arXiv: https://arxiv.org/abs/2303.06232 · HTML: https://mlsanity.com/paper/2303.06232/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 29 references that are in the map.
- First posted on arXiv 10 Mar 2023.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks](https://mlsanity.com/paper/1807.03888/) (2018)
- [Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks](https://mlsanity.com/paper/1706.02690/) (2017)
- [Energy-based Out-of-distribution Detection](https://mlsanity.com/paper/2010.03759/) (2020)
- [Deep Anomaly Detection with Outlier Exposure](https://mlsanity.com/paper/1812.04606/) (2018)
- [Out-of-Distribution Detection with Deep Nearest Neighbors](https://mlsanity.com/paper/2204.06507/) (2022)
- [ReAct: Out-of-distribution Detection With Rectified Activations](https://mlsanity.com/paper/2111.12797/) (2021)
- [Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Data](https://mlsanity.com/paper/2002.11297/) (2020)
- [ViM: Out-Of-Distribution with Virtual-logit Matching](https://mlsanity.com/paper/2203.10807/) (2022)
- [On the Importance of Gradients for Detecting Distributional Shifts in the Wild](https://mlsanity.com/paper/2110.00218/) (2021)
- [VOS: Learning What You Don't Know by Virtual Outlier Synthesis](https://mlsanity.com/paper/2202.01197/) (2022)
- [MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space](https://mlsanity.com/paper/2105.01879/) (2021)
- [A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection](https://mlsanity.com/paper/2106.09022/) (2021)
- [DICE: Leveraging Sparsification for Out-of-Distribution Detection](https://mlsanity.com/paper/2111.09805/) (2021)
- [Unsupervised Out-of-Distribution Detection by Maximum Classifier Discrepancy](https://mlsanity.com/paper/1908.04951/) (2019)
- [Semantically Coherent Out-of-Distribution Detection](https://mlsanity.com/paper/2108.11941/) (2021)
- [Improving Reconstruction Autoencoder Out-of-distribution Detection with Mahalanobis Distance](https://mlsanity.com/paper/1812.02765/) (2018)
- [Feature Space Singularity for Out-of-Distribution Detection](https://mlsanity.com/paper/2011.14654/) (2020)
- [Label-Aware Ranked Loss for robust People Counting using Automotive in-cabin Radar](https://mlsanity.com/paper/2110.05876/) (2021)
- [Near out-of-distribution detection for low-resolution radar micro-Doppler signatures](https://mlsanity.com/paper/2205.07869/) (2022)

## Built on by (most cited first, 9 of 9)

- [HOOD: Real-Time Human Presence and Out-of-Distribution Detection Using FMCW Radar](https://mlsanity.com/paper/2308.02396/) (2023)
- [HAROOD: Human Activity Classification and Out-of-Distribution Detection with Short-Range FMCW Radar](https://mlsanity.com/paper/2312.08894/) (2023)
- [Out-of-Distribution Radar Detection in Compound Clutter and Thermal Noise through Variational Autoencoders](https://mlsanity.com/paper/2503.04861/) (2025)
- [FOOD: Facial Authentication and Out-of-Distribution Detection with Short-Range FMCW Radar](https://mlsanity.com/paper/2406.04546/) (2024)
- [Class Relevance Learning For Out-of-distribution Detection](https://mlsanity.com/paper/2401.01021/) (2023)
- [FERT: Real-Time Facial Expression Recognition with Short-Range FMCW Radar](https://mlsanity.com/paper/2411.11619/) (2024)
- [FARE: A Deep Learning-Based Framework for Radar-based Face Recognition and Out-of-distribution Detection](https://mlsanity.com/paper/2501.08440/) (2025)
- [Out-of-Distribution Radar Detection with Complex VAEs: Theory, Whitening, and ANMF Fusion](https://mlsanity.com/paper/2601.18677/) (2026)
- [Training-Free Out-of-Distribution Detection for Pathology Whole-Slide Images](https://mlsanity.com/paper/2608.01407/) (2026)

## Abstract (arXiv)

Out-of-distribution (OOD) detection has recently received special attention due to its critical role in safely deploying modern deep learning (DL) architectures. 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. The simple idea is called respiration detector (RESPD) and eases the OOD detection, especially for human sitting and standing classes. On our dataset collected by 60GHz short-range FMCW Radar, we achieve AUROCs of 97.45%, 92.13%, and 96.58% for sitting, standing, and walking classes, respectively. We perform extensive experiments and show that our method outperforms state-of-the-art (SOTA) OOD detection methods. Also, our pipeline performs 24 times faster than the second-best method and is very suitable for real-time processing.