# MMW-Carry: Enhancing Carry Object Detection through Millimeter-Wave Radar-Camera Fusion

arXiv: https://arxiv.org/abs/2402.15897 · HTML: https://mlsanity.com/paper/2402.15897/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2024.

## Built on by (most cited first, 1 of 1)

- [mmAnomaly: Leveraging Visual Context for Robust Anomaly Detection in the Non-Visual World with mmWave Radar](https://mlsanity.com/paper/2604.00382/) (2026)

## Abstract (arXiv)

This paper introduces MMW-Carry, a system designed to predict the probability of individuals carrying various objects using millimeter-wave radar signals, complemented by camera input. The primary goal of MMW-Carry is to provide a rapid and cost-effective preliminary screening solution, specifically tailored for non-super-sensitive scenarios. Overall, MMW-Carry achieves significant advancements in two crucial aspects. Firstly, it addresses localization challenges in complex indoor environments caused by multi-path reflections, enhancing the system's overall robustness. This is accomplished by the integration of camera-based human detection, tracking, and the radar-camera plane transformation for obtaining subjects' spatial occupancy region, followed by a zooming-in operation on the radar images. Secondly, the system performance is elevated by leveraging long-term observation of a subject. This is realized through the intelligent fusion of neural network results from multiple different-view radar images of an in-track moving subject and their carried objects, facilitated by a proposed knowledge-transfer module. Our experiment results demonstrate that MMW-Carry detects objects with an average error rate of 25.22% false positives and a 21.71% missing rate for individuals moving randomly in a large indoor space, carrying the common-in-everyday-life objects, both in open carry or concealed ways. These findings affirm MMW-Carry's potential to extend its capabilities to detect a broader range of objects for diverse applications.