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

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.

Learning to Detect Open Carry and Concealed Object with 77GHz Radar

Detecting harmful carried objects plays a key role in intelligent surveillance systems and has widespread applications, for example, in airport security. In this paper, we focus on the relatively unexplored area of using low-cost 77GHz mmWave radar for the carried objects detection problem. The proposed system is capable of real-time detecting three classes of objects - laptop, phone, and knife - under open carry and concealed cases where objects are hidden with clothes or bags. This capability is achieved by initial signal processing for localization and generating range-azimuth-elevation image cubes, followed by a deep learning-based prediction network and a multi-shot post-processing module for detecting objects. Extensive experiments for validating the system performance on detecting open carry and concealed objects have been presented with a self-built radar-camera testbed and dataset. Additionally, the influence of different input, factors, and parameters on system performance is analyzed, providing an intuitive understanding of the system. This system would be the very first baseline for other future works aiming to detect carried objects using 77GHz radar.