REAL-TIME HAND GESTURE DETECTION AND RECOGNITION USING CONVOLUTIONAL ARTIFICIAL NEURAL NETWORKS
DETEKSI DAN PENGENALAN GESTUR TANGAN SECARA REAL-TIME MENGGUNAKAN JARINGAN SARAF TIRUAN KONVOLUSIONAL
DOI:
https://doi.org/10.46880/mtk.v9i2.1911Keywords:
Hand gesture detection, Movement recognition, Computer VisionAbstract
Real-time hand gesture detection is an interesting topic in pattern recognition and computer vision. In this study, we propose the use of a Convolutional Neural Network (CNN) to detect and recognize hands in real-time. Our goal is to develop a system that can accurately identify and interpret user gestures in real-time. The proposed approach involves two main stages, namely hand gesture recognition and gesture recognition. For stage detection, we use the CNN architecture to recognize hands in the video. We train the CNN model using a dataset containing various hand gestures. Once a hand is detected, we extract the relevant hand region and proceed to the gesture recognition stage. The gesture recognition stage involves training and testing CNN models for different hand signal recognition. We use a hand gesture dataset that contains a variety of common hand signals. The experimental results show that the proposed system can detect and recognize hand movements in real-time with satisfactory accuracy. Although there are still some challenges that need to be overcome, this research provides a solid foundation for further development in real-time hand gesture recognition.
References
Wibowo, Arif & Astuti, Dina. (2021). Gestur Tangan Manusia dalam Karya Fotografi Seni. Rekam. 17. 113-122. 10.24821/rekam.v17i2.4803.
Nurhayati, Oky & Eridani, Dania & Tsalavin, Muhammad. (2022). Sistem Isyarat Bahasa Indonesia (SIBI) Metode Convolutional Neural Network Sequential secara Real Time. Jurnal Teknologi Informasi dan Ilmu Komputer. 9. 819. 10.25126/jtiik.2022944787.
Yirtici, Tolga & Yurtkan, Kamil. (2022). Regional-CNN-based enhanced Turkish sign language recognition. Signal, Image and Video Processing. 16. 1-7. 10.1007/s11760-021-02082-2.
Saiful, Md & Isam, Abdulla & Moon, Hamim & Tammana, Rifa & Das, Mitul & Alam, Md & Rahman, Ashifur. (2022). Real-Time Sign Language Detection Using CNN. 10.1109/ICDABI56818.2022.10041711.
Roihan, Ahmad & Sunarya, Po & Rafika, Ageng. (2020). Pemanfaatan Machine Learning dalam Berbagai Bidang: Review paper. IJCIT (Indonesian Journal on Computer and Information Technology). 5. 10.31294/ijcit.v5i1.7951.
Somvanshi, M., & Chavan, P. (2016). A review of machine learning techniques using decision tree and support vector machine. 2016 International Conference on Computing Communication Control and Automation (ICCUBEA), 1–7. https://doi.org/10.1109/ICCUBEA.2016.7860040
Amei, W., Huailin, D., Qingfeng, W., & Ling, L. (2011). A survey of application-level protocol identification based on machine learning. 2011 International Conference on Information Management, Innovation Management and Industrial Engineering, 3, 201–204.
Naeem, Samreen & Ali, Aqib & Anam, Sania & Ahmed, Munawar. (2023). An Unsupervised Machine Learning Algorithms: Comprehensive Review. 13. 911-921. 10.12785/ijcds/130172.
Renee, Tong & Yukai, Feng & Wang, Jian & Wu, Zhengxing & Tan, M. & Yu, Junzhi. (2023). A Survey on Reinforcement Learning Methods in Bionic Underwater Robots. Biomimetics. 8. 168. 10.3390/biomimetics8020168.
Qiang, W., & Zhongli, Z. (2011). Reinforcement learning model, algorithms and its application. 2011 International Conference on Mechatronic Science, Electric Engineering and Computer (MEC), 1143–1146.
Felisa, Jenisa & Setiawan, Dhanny & Khalisa, Iffa. (2023). Perancangan Perangkat Lunak Pengenalan Karakter Plat Nomor Kendaraan dengan Metode Convolutional Neural Network. Media Informatika. 21. 280-306. 10.37595/mediainfo.v21i3.156.
Lin, Xiyu. (2023). Research of Convolutional Neural Network on Image Classification. Highlights in Science, Engineering and Technology. 39. 855-862. 10.54097/hset.v39i.6656.
Sahoo, Jaya & Prakash, Allam & Pławiak, Paweł & Samantray, Saunak. (2022). Real-Time Hand Gesture Recognition Using Fine-Tuned Convolutional Neural Network. Sensors. 22. 706. 10.3390/s22030706.
Utomo, A. S., Yudhana, A. A., & Wibowo, F. (2020). Implementasi Algoritma Random Forest dalam Pengklasifikasian Data Penderita Diabetes. Jurnal Sains dan Seni ITS, 9(2), B100-B105.
Hidayatullah, R., & Arifin, A. Z. (2020). Analisis Klasifikasi Data Pemantauan Jantung Menggunakan Algoritma K-Nearest Neighbor. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 4(4), 829-836.
El-Sayed, H., & Elhoseny, M. (2021). Deep learning architectures for medical image classification: An overview. Future Computing and Informatics Journal, 6(1), 1-18.
Prabha, M., & Kuppusamy, K. S. (2021). Real-Time Hand Gesture Recognition using Convolutional Neural Networks. International Journal of Advanced Trends in Computer Science and Engineering, 10(2), 466-472.
Huang, C., Liu, X., Tang, S., Liu, M., & Qin, C. (2020). Convolutional Neural Networks: A Comprehensive Review. ACM Transactions on Multimedia Computing, Communications, and Applications, 16(2), 1-28.
Baktash, F., Zawbaa, H. M., & Jemai, O. (2020). Hand Gesture Recognition for Emotion Detection Using Deep Learning Techniques. Applied Sciences, 10(7), 2517.
Wei, B., Li, J., Chen, H., & Wang, J. (2021). Hand Gesture Recognition for Human-Robot Interaction Based on Deep Learning. IEEE Access, 9, 72129-72142.
Adhianto, L., & Nasution, M. F. (2021). Penerapan Metode Ensemble dalam Klasifikasi Kelas pada Dataset Absensi Mahasiswa. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 5(3), 725-732.
Hidayatullah, R., & Arifin, A. Z. (2020). Analisis Klasifikasi Data Pemantauan Jantung Menggunakan Algoritma K-Nearest Neighbor. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 4(4), 829-836.
Reddy, N. R. V., Dwarapudi, A., & Kannala, R. (2020). Vision-based perception and control for autonomous navigation: a survey. Journal of Ambient Intelligence and Humanized Computing, 11(4), 1719-1740.
Saeed, F., Nazir, M., Aslam, N., & Azeem, M. (2021). Artificial Intelligence Based Computer Vision Techniques for Vehicle Recognition: A Survey. Indonesian Journal of Electrical Engineering and Computer Science, 24(1), 314-324.
Setyawan, F. A., Natsir, R., & Afriani, R. (2020). Optimasi Kinerja Mesin Pembelajaran dengan Algoritma Kombinasi Particle Swarm Optimization dan K-Nearest Neighbor. Jurnal Teknologi dan Sistem Komputer, 8(1), 1-7.
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Copyright (c) 2023 Gidion Bagas Prananta, Hagi Azzam Azzikri, Chaerur Rozikin
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