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Internet Things"],"published-print":{"date-parts":[[2024,11,30]]},"abstract":"<jats:p>\n            Object detection plays a pivotal role in various fields, for example, a smart traffic system relies on the detected results for decision-making. However, existing studies predominately utilize optical camera and LiDAR, which exhibit limitations in adverse outdoor environments, such as foggy weather. To address these challenges, millimeter-waves (mmWaves) attract researchers\u2019 attention to detect objects in severe conditions since they can work effectively in low-visibility conditions and overcome small obstacles. Yet, previous mmWave-based works have shown limited performance, such as no shape information for objects. Therefore, we design and implement a two-stage system,\n            <jats:italic>mmBox<\/jats:italic>\n            , to accurately predict bounding boxes with depth for vehicles and pedestrians, which first generates heatmaps in different dimensions and then leverages a deep learning model to extract features for predictions. To evaluate the performance of\n            <jats:italic>mmBox<\/jats:italic>\n            , we collected real-world mmWave reflections from urban traffic intersections and dense-fog environments. The extensive evaluation metrics show remarkable accuracy and the low latency of our model.\n          <\/jats:p>","DOI":"10.1145\/3695883","type":"journal-article","created":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T03:55:45Z","timestamp":1726199745000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["mmBox: Harnessing Millimeter-Wave Signals for Reliable Vehicle and Pedestrians Detection"],"prefix":"10.1145","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7331-1537","authenticated-orcid":false,"given":"Zhuangzhuang","family":"Gu","sequence":"first","affiliation":[{"name":"Computer Science and Engineering, University of South Carolina, Columbia, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9353-2011","authenticated-orcid":false,"given":"Hem","family":"Regmi","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, University of South Carolina, Columbia, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5711-3087","authenticated-orcid":false,"given":"Sanjib","family":"Sur","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, University of South Carolina, Columbia, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,10,24]]},"reference":[{"key":"e_1_3_2_2_2","article-title":"Pedestrian safety at signalized intersections: Spatial and machine learning approaches","volume":"24","author":"Ku\u015fkapan Emre","year":"2022","unstructured":"Emre Ku\u015fkapan, Mohammad Ali Sahraei, Merve Kayaci \u00c7odur, and Muhammed Yasin \u00c7odur. 2022. 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