A Comparison of Anfis and Fuzzy Classifiers for Object Tracking Based on RGB Color Using Camera Vision

Authors

  • Iqbal Robiyana Department of Physics, Faculty of Science, Universitas Mandiri, Subang Indonesia
  • Timbo Faritcan Parlaungan Department of Informatic, Faculty of Engineering, Universitas Mandiri, Subang Indonesia
  • Sarifudin Department of Physics, Faculty of Science, Universitas Mandiri, Subang Indonesia
  • Muhamad Agung Suhendra Universitas Mandiri

DOI:

https://doi.org/10.11594/timeinphys.2023.v1i2p85-91

Keywords:

Image Processing, Fuzzy, Anfis, Image Substracting, Region Properties

Abstract

Image processing technology has a wide range of applications, such as in the medical, military, surveillance, and robotics industries. Analyzing objects in images is crucial when it comes to image processing. This study focuses on image processing to track objects of red, green, and blue (RGB) colors through the utilization of a camera. There are two research schemes: image processing and data classification. The data classification method used is the fuzzy and adaptive neuro-fuzzy inference system (ANFIS). The methods of image subtracting and region properties are commonly utilized for image processing. Based on the classification data results, the fuzzy logic classification demonstrated a higher accuracy rate of 86% when compared to Anfis' 65%. This was observed when both classification models were tested using a random sample. The value of Anfis is small due to the limited size of the training data used. As a result, it is recommended to use a fuzzy classifier for object color tracking for good performance.

References

Hani Hunud A. K., Yasir Mohd M. (2013). Colour-based Object Detection and Tracking for Autonomous Quadrotor UAV. IOP Conf. Ser.:Mater. Sci. Eng. 53. doi: 10.1088/1757-899X/53/1/012086

Yunus C., Mahmut A., Mahit G. (2017) Color based moving object tracking with an active camera using motion information. IEEE Explore. doi:10.1109/IDAP.2017.8090332

Ali Basrah P., Zhfranul N., Muhammad A., Hastuti, Hamdani, Dwiprima E. M. (2021). Object Detection with A Webcam Using the Python Programming Language. Journal of Applied Engineering and Technological Science. Vol 2(2), 103-111.

Mai Thanh N. T., Sanghoon K. (2017). Parallel implementation of color-based particle filter for object tracking in embedded systems. Human-centric Computing and Information Sciences volume. doi : 10.1186/s13673-016-0082-1

Goli A., Hossein A., Mehrbakhsh N., Tarik A. R., Omed H., Nahla A., Azida Z. (2019). Fuzzy logic approach for infectious disease diagnosis: A methodical evaluation, literature and classification. Biocybern Biomed Eng.39(4): 937–955. doi: 10.1016/j.bbe.2019.09.004

P. Thirumurugan, P. Shanthakumar. (2016). Brain tumor detection and diagnosis using ANFIS classifier. IMA.Vol. 26(2), doi: https://doi.org/10.1002/ima.22170

Suhendra, M. A., Parlaungan, T. F., & Sumardi, T. (2023). Voice Recognition as a Mobile Robot Controller with the Adaptive Neuro-Fuzzy Inference System Method. TIME in Physics, 1(1), 43–49. https://doi.org/10.11594/timeinphys.2023.v1i1p43-49

A. Turnip, M. Agung S., and Mada Sanjaya W. S. (2015). Brain-Controlled Wheelchair based EEG SSVEP Signals Classified by Nonlinear Adaptive Filter. In Proc. 14th IEEE/RAS-EMBS International Conference on Rehabilitation Robotics, pp. 905-908.

A. Turnip, M. Faizal A., M. Agung S., and Dwi Esti K. (2017). Lie Detection Based EEG-P300 Signal Classified by ANFIS Method, Journal of Telecommunication, Electronic and Computer Engineering (JTEC), Vol. 9, pp. 107-110.

T. V. Padmavathy, M. N. Vimalkumar, D. S. Bhargava. (2018). Adaptive clustering-based breast cancer detection with ANFIS classifier using mammographic images. Cluster Computing. doi: https://doi.org/10.1007/s10586-018-2160-9

Eka A., Wiwien H., Zuli B. (2013) Implementasi Metode Image Subtracting dan Metode Regionprops untuk Mendeteksi Jumlah Objek Berwarna RGB pada File Video. Jurnal Teknologi Informasi DINAMIK. Volume 18, No.2, Juli 2013: 91-100

Shahbe M. D., Qussay A. S. (2004). Image Subtraction for Real Time Moving Object Extraction. International Conference on Computer Graphics, Imaging and Visualization (CGIV’04), doi: http://dx.doi.org/10.1109/CGIV.2004.1323958

Ahmed R. N., Ahmad T. A., Amjad J. H., Ammar K. A. Ibraheem K. I. (2021). Intelligent Fault Detection and Identification Approach for Analog Electronic Circuits Based on Fuzzy Logic Classifier. Electronics,10(23), 2888; https://doi.org/10.3390/electronics10232888

Afshin S., Navid G., Marjane K., Parisa M., Roohallah A., Assef Z., Abbas K., Abdulhamit S., U. Rajendra A., J. Manuel G. (2022). Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropies. Biomedical Signal Processing and Control, 73, 103417. doi: https://doi.org/10.1016/j.bspc.2021.103417

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Published

2023-08-18

How to Cite

Iqbal Robiyana, Timbo Faritcan Parlaungan, Sarifudin, & Suhendra, M. A. (2023). A Comparison of Anfis and Fuzzy Classifiers for Object Tracking Based on RGB Color Using Camera Vision. TIME in Physics, 1(2), 85–91. https://doi.org/10.11594/timeinphys.2023.v1i2p85-91

Issue

Vol. 1 No. 2 (2023): August

Section

Articles

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Copyright (c) 2023 Iqbal Robiyana, Timbo Faritcan Parlaungan, Sarifudin, Muhamad Agung Suhendra

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