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Vision-Based Human Presence Detection by Means of Transfer Learning Approach

Vision-Based Human Presence Detection by Means of Transfer L...

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Innovative Manufacturing, Mechatronics and Materials Forum, iM3F 2021

作者： Tang, Jin Cheng Ab. Nasir, Ahmad Fakhri P. P. Abdul Majeed, Anwar Mohd Razman, Mohd Azraai Mohd Khairuddin, Ismail Lim, Thai Li Faculty of Manufacturing and Mechatronic Engineering Technology Universiti Malaysia Pahang Pahang Pekan26600 Malaysia Faculty of Computing Universiti Malaysia Pahang Pahang Pekan26600 Malaysia Centre for Software Development and Integrated Computing Universiti Malaysia Pahang Pahang Gambang26600 Malaysia TT Vision Holdings Berhad 11900 Plot 106 Sungai Hilir Keluang 5 Bayan Lepas FIZ.4 Pulau Pinang Bayan Lepas Malaysia Xi’an Jiaotong-Liverpool University Suzhou215123 China

ISBN: (纸本)9789811920943

Human–robot interaction (HRI) and human–robot collaboration (HRC) has become more important as the industries are stepping towards the phase of digitalization and Industry 4.0. Indeed, the emphasis is often placed on the safety of the physical well-being of the human workers. To safeguard the human operators from being hurt by the robots or collaborative robots (cobots), a traditional method is to isolate the robots from the human workers by means of fences and sensors. However, the deployment of deep learning models is unknown and shown to be non-trivial in downstream tasks such as image classification and object detection. The present study aimed to exploit the effectiveness of object detection models, particularly EfficientDet models via a transfer learning approach—fine-tuning. A total of 1463 images were obtained from the surveillance cameras from TT Vision Holdings Berhad and split into training, validation, and test by a ratio of 70:20:10. The training images were further augmented using horizontal flip and scale jittering techniques to increase the total training images up to 3072 images. As an outcome, the result revealed that the EfficientDet-D2 fine-tuned model achieved a test AP of 81.70% with an inference speed of 97.06 ms on Tesla T4 while the EfficientDet-D0 fine-tuned model attained a test AP of 69.30% with an inference speed of 30.24 ms on Tesla T4. In comparison between the EfficientDet-D0 fine-tuned model and EfficientDet-D2 fine-tuned model, the performance improved in terms of AP with the inference speed as the trade-off. The research has shown that it is feasible to detect the presence of human workers and can possibly serve as the visual perception of the robot with regards to human presence detection. Last but not least, the present work proved the applicability of transfer learning methods in human presence detection, specifically fine-tuned object detection models. © 2022, The Author(s), under exclusive license to Springer Nature Singapore

关键词： Object recognition

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The Effect of Image Input Transformation from Inertial Measurement Unit Data on the Classification of Skateboarding Tricks 8th

The Effect of Image Input Transformation from Inertial Measu...

引用

8th International Conference on Robot Intelligence Technology and Applications, RiTA 2020

作者： Abdullah, Muhammad Amirul Ibrahim, Muhammad Ar Rahim Shapiee, Muhammad Nur Aiman Zakaria, Muhammad Aizzat Mohd Razman, Mohd Azraai Musa, Rabiu Muazu P. P. Abdul Majeed, Anwar Innovative Manufacturing Mechatronics and Sports Laboratory Faculty of Manufacturing and Mechatronic Engineering Technology Universiti Malaysia Pahang Pahang Malaysia Centre for Fundamental and Continuing Education Department of Credited Co-Curriculum Universiti Malaysia Terengganu Terengganu Malaysia Centre for Software Development and Integrated Computing Universiti Malaysia Pahang Gambang26000 Malaysia

ISBN: (纸本)9789811648021

This study aims to improve the classification of flat-ground tricks namely Ollie, Kickflip, Shove-it, Nollie and Frontside 180 through the identification of significant input image transformation on different transfer learning models. Six goofy skateboarders (23 years of age ± 5.0 years’ experience) executed five tricks for each type of trick repeatedly on a customized ORY skateboard (Inertial Measurement Unit (IMU) sensor fused) on a cemented ground. From the IMU data, six raw signals were extracted. The best input image transformation and transfer learning model were identified through two input image transformations synthesized, namely raw data (RAW) and Fast Fourier Transform (FFT), and six transfer learning models based on default arguments from the Keras library. The variation of the SVM models (via different hyperparameters) was evaluated both on input image transformation and on transfer learning model in classifying the skateboarding tricks. It was shown from the study that RAW input image on three transfer learning models (DenseNet121, InceptionResNetV2, and ResNet101) demonstrated the 100% accuracy on all train, train and validation dataset. It could be concluded that the proposed method is able to improve the classification of the skateboarding tricks well. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification Machine learning Skateboarding Support Vector Machine Transfer learning

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