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Titlebook: Deep Learning for Human Activity Recognition; Second International Xiaoli Li,Min Wu,Le Zhang Conference proceedings 2021 Springer Nature Si

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21#
發(fā)表于 2025-3-25 05:44:34 | 只看該作者
22#
發(fā)表于 2025-3-25 07:53:37 | 只看該作者
Nigeria: The State that Lost Its Futureman actions. Results on UCF-Sports and UR Fall dataset present comparable accuracy to State-of-the-Art approaches with significantly lower model size and computation demand and the ability for real-time execution on edge embedded device (e.g. Nvidia Jetson Xavier).
23#
發(fā)表于 2025-3-25 14:32:05 | 只看該作者
Single Run Action Detector over Video Stream - A Privacy Preserving Approach,man actions. Results on UCF-Sports and UR Fall dataset present comparable accuracy to State-of-the-Art approaches with significantly lower model size and computation demand and the ability for real-time execution on edge embedded device (e.g. Nvidia Jetson Xavier).
24#
發(fā)表于 2025-3-25 18:10:27 | 只看該作者
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發(fā)表于 2025-3-25 20:44:11 | 只看該作者
Deep Learning for Human Activity Recognition978-981-16-0575-8Series ISSN 1865-0929 Series E-ISSN 1865-0937
26#
發(fā)表于 2025-3-26 00:20:28 | 只看該作者
27#
發(fā)表于 2025-3-26 07:01:31 | 只看該作者
978-981-16-0574-1Springer Nature Singapore Pte Ltd. 2021
28#
發(fā)表于 2025-3-26 10:55:07 | 只看該作者
29#
發(fā)表于 2025-3-26 13:07:16 | 只看該作者
Conducting Multiple Comparisonsevices. Many papers presented various techniques for human activity representation that resulted in distinguishable progress. In this study, we conduct an extensive literature review on recent, top-performing techniques in human activity recognition based on wearable sensors. Due to the lack of stan
30#
發(fā)表于 2025-3-26 19:06:26 | 只看該作者
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