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- W4386004730 abstract "Human detection plays an important role in several fields (such as autonomous mobile robots, bio-medical applications, military applications, etc.) and has received considerable attention from researchers in recent years. Especially, human gesture recognition provides information to predict human behavior for collision avoidance of the robots. The present paper proposes an approach in which deep-learning method and machine-learning method are integrated to classify activities and movements for multiple human targets. The proposed recognition process involves three sequential steps, namely the YOLOv5 model for detecting targets, the Media Pipe for drawing the skeleton, and the LSTM network for recognizing activities. The proposed method is examined through different scenarios. In the case of detecting the target with YOLOv5, the experimental results show that the loss accuracy always maintains below 5% for both training and the validation processes, and the mean Average Precision (mAP) of the designed YOLOv5 model is always higher than 99% for all consideration case studies. Furthermore, the activity recognition performance of the proposed method also successfully detects and tracks the behavior of the defined target inspected via three case studies: sitting, standing, and hands up. The experimental results prove the stability and precision of the method and point out that this approach can be applied to further studies and applications." @default.
- W4386004730 created "2023-08-20" @default.
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- W4386004730 date "2023-01-01" @default.
- W4386004730 modified "2023-10-12" @default.
- W4386004730 title "Multiple Target Activity Recognition by Combining YOLOv5 with LSTM Network" @default.
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- W4386004730 doi "https://doi.org/10.1007/978-981-99-4725-6_49" @default.
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