Human Action Recognition of Autonomous Mobile Robot Using Edge-AI
學年 111
學期 1
出版（發表）日期 2023-01-15
作品名稱 Human Action Recognition of Autonomous Mobile Robot Using Edge-AI
作品名稱（其他語言）
著者 Shih-Ting Wang; I-Hsum Li; Wei-Yen Wang
單位
出版者
著錄名稱、卷期、頁數 IEEE Sensors Journal 23(2), p.1671-1682
摘要 The development of autonomous mobile robots (AMRs) has brought with its requirements for intelligence and safety. Human action recognition (HAR) within AMR has become increasingly important because it provides interactive cognition between human and AMR. This study presents a full architecture for edge-artificial intelligence HAR (Edge-AI HAR) to allow AMR to detect human actions in real time. The architecture consists of three parts: a human detection and tracking network, a key frame extraction function, and a HAR network. The HAR network is a cascade of a DenseNet121 and a double-layer bidirectional long-short-term-memory (DLBiLSTM), in which the DenseNet121 is a pretrained model to extract spatial features from action key frames and the DLBiLSTM provides a deep two-directional LSTM inference to classify complicated time-series human actions. Edge-AI HAR undergoes two optimizations—ROS distributed computation and TensorRT structure optimization—to give a small model structure and high computational efficiency. Edge-AI HAR is demonstrated in two experiments using an AMR and is demonstrated to give an average precision of 97.58% for single action recognition and around 86% for continuous action recognition.
關鍵字 Autonomous mobile robot (AMR);bidirectional long-short-term-memory (BiLSTM);edge artificial intelligence (Edge AI);human action recognition (HAR);ROS
語言 en_US
ISSN 1558-1748; 1530-437X
期刊性質 國外
收錄於
產學合作
通訊作者
審稿制度 否
國別 USA
公開徵稿
出版型式 ,電子版,紙本
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機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/123247 )

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