A Systematic Study on Object Recognition Using Millimeter-wave Radar

Thumbnail Image

Files

2305.02085v1.pdf (3.13 MB)

Links to Files

https://ieeexplore.ieee.org/document/10207645

Permanent Link

https://doi.org/10.1109/SMARTCOMP58114.2023.00025
 http://hdl.handle.net/11603/28083

Collections

UMBC Computer Science and Electrical Engineering Department
UMBC Center for Real-time Distributed Sensing and Autonomy
UMBC Faculty Collection
UMBC Information Systems Department
UMBC Student Collection

Author/Creator

Author/Creator ORCID

https://orcid.org/0000-0002-1290-0378
 https://orcid.org/0000-0002-8495-0948

Date

2023-08-07

Type of Work

conference papers and proceedings
preprints
Text

Department

Program

Citation of Original Publication

Devnath, Maloy Kumar, Avijoy Chakma, Mohammad Saeid Anwar, Emon Dey, Zahid Hasan, Marc Conn, Biplab Pal, and Nirmalya Roy. “A Systematic Study on Object Recognition Using Millimeter-Wave Radar.” In 2023 IEEE International Conference on Smart Computing (SMARTCOMP), 57–64, 2023. https://doi.org/10.1109/SMARTCOMP58114.2023.00025.

Rights

© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Subjects

Abstract

Millimeter-wave (MMW) radar is becoming an essential sensing technology in smart environments due to its light and weather-independent sensing capability. Such capabilities have been widely explored and integrated with intelligent vehicle systems, often deployed in industry-grade MMW radars. However, industry-grade MMW radars are often expensive and difficult to attain for deployable community-purpose smart environment applications. On the other hand, commercially available MMW radars pose hidden underpinning challenges that are yet to be well investigated for tasks such as recognizing objects, and activities, real-time person tracking, object localization, etc. Such tasks are frequently accompanied by image and video data, which are relatively easy for an individual to obtain, interpret, and annotate. However, image and video data are light and weather-dependent, vulnerable to the occlusion effect, and inherently raise privacy concerns for individuals. It is crucial to investigate the performance of an alternative sensing mechanism where commercially available MMW radars can be a viable alternative to eradicate the dependencies and preserve privacy issues. Before championing MMW radar, several questions need to be answered regarding MMW radar’s practical feasibility and performance under different operating environments. To answer the concerns, we have collected a dataset using commercially available MMW radar, Automotive mmWave Radar (AWR2944) from Texas Instruments, and reported the optimum experimental settings for object recognition performance using several deep learning algorithms in this study. Moreover, our robust data collection procedure allows us to systematically study and identify potential challenges in the object recognition task under a cross-ambience scenario. We have explored the potential approaches to overcome the underlying challenges and reported extensive experimental results.