A Radar-Based Human Activity Recognition Using a Novel 3-D Point Cloud Classifier

This article provides a new benchmark dataset for 3-D point cloud classification in which the manually labeled human activity data exceeds 100 point clouds per frame and is capable of meeting the training needs for data-intensive learning approaches. In this study, a case study is considered for eva...

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Vydáno v:	IEEE sensors journal Ročník 22; číslo 19; s. 18218 - 18227
Hlavní autoři:	Yu, Zheqi, Taha, Ahmad, Taylor, William, Zahid, Adnan, Rajab, Khalid, Heidari, Hadi, Imran, Muhammad Ali, Abbasi, Qammer H.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.10.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Benchmark dataset Benchmark testing Benchmarks Data collection Datasets Deep learning density-based spatial clustering of applications with noise (DBSCAN) Human activity recognition human activity recognition (HAR) long short-term memory (LSTM) Machine learning Motion perception Neural networks NodeNs sensor Point cloud compression Radar Sensors Smart phones Three dimensional models Training Wearable computers
ISSN:	1530-437X, 1558-1748
On-line přístup:	Získat plný text
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