A Systematic Study on Object Recognition Using Millimeter-wave Radar
A Systematic Study on Object Recognition Using Millimeter-wave Radar
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DOI：
10.1109/smartcomp58114.2023.00025
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发表时间：
2023-05
期刊：
影响因子：
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通讯作者：
Maloy Kumar Devnath;Avijoy Chakma;Mohammad Saeid Anwar;E. Dey;Zahid Hasan;Marc Conn;Biplab Pal;Nirmalya Roy
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文献类型：
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作者：
Maloy Kumar Devnath;Avijoy Chakma;Mohammad Saeid Anwar;E. Dey;Zahid Hasan;Marc Conn;Biplab Pal;Nirmalya Roy
毫米波（MMW）雷达由于其与光和天气无关的传感能力，正在成为智能环境中必不可少的传感技术。这种能力已经被广泛探索并集成到智能车辆系统中，通常部署在工业级毫米波雷达中。然而，工业级毫米波雷达通常价格昂贵，难以实现可部署的社区智能环境应用。另一方面，商用毫米波雷达带来了隐藏的基础挑战，这些挑战尚未得到很好的研究，例如识别物体、活动、实时人员跟踪、物体定位等任务。这些任务通常伴随着图像和视频数据，这些数据对于个人来说相对容易获取、解释和注释。然而，图像和视频数据依赖于光和天气，容易受到遮挡效应的影响，并且固有地引起了个人隐私问题。研究一种替代传感机制的性能至关重要，其中商用毫米波雷达可以成为消除依赖关系和保护隐私问题的可行替代方案。在支持毫米波雷达之前，需要回答有关毫米波雷达在不同工作环境下的实际可行性和性能的几个问题。为了回答这些问题，我们收集了一个使用商用毫米波雷达的数据集，即德州仪器公司的汽车毫米波雷达（AWR2944），并在本研究中使用几种深度学习算法报告了目标识别性能的最佳实验设置。此外，我们强大的数据收集程序使我们能够系统地研究和识别跨环境场景下物体识别任务中的潜在挑战。我们已经探索了克服潜在挑战的潜在方法，并报告了广泛的实验结果。
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.