Pengenalan Personal Menggunakan Citra Tampak Atas pada Lingkungan Cashierless Strore
DOI:
https://doi.org/10.24002/jbi.v10i1.1779Abstract
Abstract.
Personal recognition with image processing techniques from the side view has the disadvantage of being applied to the cashierless store environment, namely inaccurate recognition or identification when personal collisions occur. To overcome this, the image capture method is used from the top-view. Personal recognition method through the top-view image using the Haar Cascade Classifier method. 1420 positive images and 2170 negative images are used to find features that are considered suitable for recognizing objects using the Adaptive Boosting (Adaboost) method. Tests were carried out on 100 test data by varying the parameters of min_neighbors (3.4, and 5) and the size of the dataset window (25x25, 35x35, 45x45 pixels). Personal recognition testing gets the highest accuracy of 89.9% with the parameters used are min_neighbors 5 and the size of the 25x25 pixel dataset in the detection parameter size of min_size 140x140 pixels.
Keywords: Person recognition, image processing, cashierless store
Abstrak.
Pengenalan personal dengan teknik pengambilan citra dari tampak samping memiliki kelemahan untuk diterapkan pada lingkungan cashierless store yaitu tidak akuratnya pengenalan atau identifikasi saat terjadi tubrukan antar personal. Untuk mengatasi hal tersebut maka dipakailah metode pengambilan citra dari tampak atas. Metode pengenalan personal melalui citra tampak atas menggunakan metode Haar Cascade Classifier. Digunakan 1420 citra positif dan 2170 citra negatif untuk menemukan fitur-fitur yang dianggap cocok untuk mengenali objek dengan menggunakan metode Adaptive Boosting (Adaboost). Pengujian dilakukan terhadap data tes sebanyak 100 citra dengan menvariasikan parameter min_neighbors (3,4, dan 5) dan ukuran window dataset (25x25, 35x35, 45x45 piksel). Pengujian pengenalan personal mendapatkan akurasi tertinggi sebesar 89,9% dengan parameter yang dipakai yaitu min_neighbors 5 dan ukuran window dataset 25x25 piksel pada parameter ukuran pengujian min_size 140x140 piksel.
Kata Kunci: pengenalan personal, pengolahan citra, cashierless store
References
W. Lan, J. Dang, Y. Wang, and S. Wang, “Pedestrian Detection Based on YOLO Network Model,” in 2018 IEEE International Conference on Mechatronics and Automation (ICMA), 2018, pp. 1547–1551.
W. Sun, S. Zhu, B. Fu, Y. Cheng, and F. Fang, “Pedestrians tracking based on least squares algorithm and intelligent collision avoidance model,” in 2017 29th Chinese Control And Decision Conference (CCDC), 2017, pp. 1760–1765.
J. Viola and M. Jones, “Rapid Object Detection using a Boosted Cascade of Simple feature,” in Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Kauai, HI, USA, 2001, pp. I-I.
A. Kadir and Susanto A., Teori dan Aplikasi Pengolahan Citra. Yogyakarta: ANDI, 2013.
D. Barik and M. Mondal, “Object identification for computer vision using image segmentationObject identification for computer vision using image segmentation,” 2010.
Deep S. et al., “Pattern based object recognition in image processing,” presented at the 2014 11th International Computer Conference on Wavelet Actiev Media Technology and Information Processing(ICCWAMTIP), 2014.
K. Khurana and R. Awasthi, “Techniques for Object Recognition in Images and Multi-Object Detection,” in International Journal of Advanced Research in Computer Engineering & Technology, 2013.
Shalima and R. Virk, “Review of Image Fusion Techniques,” in International Research Journal of Engineering and TechnologyInternational Research Journal of Engineering and Technology, 2015.
Natakani, R. et al, “A Person Identification Method Using a Top-View Head Image from an Overhead Camera,” in Journal of Advanced Computational Intelligence and Intelligent Informatics, 2012, vol. 16(6), pp. 696–703.
M. Rauter, “Reliable Human Detection and Tracking in Top-View Depth Images,” in 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2013, pp. 529–534.
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