Learning to Detect Open Carry and Concealed Object with 77GHz Radar
- URL: http://arxiv.org/abs/2111.00551v1
- Date: Sun, 31 Oct 2021 17:33:28 GMT
- Title: Learning to Detect Open Carry and Concealed Object with 77GHz Radar
- Authors: Xiangyu Gao, Hui Liu, Sumit Roy, Guanbin Xing, Ali Alansari, Youchen
Luo
- Abstract summary: This paper focuses 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.
This system would be the very first baseline for other future works aiming to detect carried objects using 77GHz radar.
- Score: 7.608789301874509
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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.
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