Comparative Analysis of ArUco Marker Detection Techniques Using Adaptive Thresholding, CLAHE, and Kalman Filter for Smart Cane Applications
DOI:
https://doi.org/10.52436/1.jutif.2025.6.4.4883Keywords:
Adaptive Thresholding, ArUco Marker, Blind and Visually Impaired, CLAHE, Kalman Filter, Smart Cane SystemAbstract
This study aims to analyze and compare the effectiveness of three image processing techniques Adaptive Thresholding, CLAHE, and Kalman Filter in enhancing the performance of ArUco marker detection for a smart cane system designed for visually impaired individuals at SLB Kuncup Mas Banyumas. The evaluation method includes detection accuracy, marker position precision, and computational time required by each technique under two different lighting conditions: daytime and nighttime. The results show that all three image processing techniques successfully achieved a 100% detection accuracy for ArUco markers. However, significant differences were observed in computational time, with Kalman Filter demonstrating the fastest processing speed, making it the most efficient option for real-time applications requiring quick response. CLAHE and Adaptive Thresholding performed better in uneven lighting conditions, although they required longer computational times. Kalman Filter is therefore recommended for marker-based navigation systems in environments demanding fast response times, while CLAHE and Adaptive Thresholding are better suited for settings with variable lighting intensities. The implications of these findings open opportunities for developing adaptive navigation systems capable of dynamically adjusting image preprocessing methods based on real-time environmental conditions. This study contributes practically to the advancement of assistive navigation technologies for visually impaired individuals, particularly in the development of visual marker-based detection systems. The results also provide a useful guideline for selecting appropriate image processing techniques according to environmental characteristics, thereby improving the accuracy and adaptability of navigation systems across diverse lighting conditions and operational environments.
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References
A. Sampathkrishna, “ArUco Maker based localization and Node graph approach to mapping,” 2022, [Online]. Available: http://arxiv.org/abs/2208.09355
A. van Putten, M. F. Giersberg, and T. B. Rodenburg, “Tracking laying hens with ArUco marker backpacks,” Smart Agric. Technol., vol. 10, 2025, doi: 10.1016/j.atech.2024.100703.
S. Afsha and M. M. Sazid, “AR-TurtleSLAM: EKF-based Localization and Mapping using ArUco Feature Detection on Mobile Robots,” 2024. doi: 10.1109/COMPAS60761.2024.10797053.
S. Chatterjee, D. Doan, and B. Calli, “Utilizing Inpainting for Training Keypoint Detection Algorithms Towards Markerless Visual Servoing,” in Proceedings - IEEE International Conference on Robotics and Automation, 2024, pp. 3086–3092. doi: 10.1109/ICRA57147.2024.10610006.
L. Yan and L. Zhao, “An approach on advanced unscented kalman filter from mobile robot-slam,” in International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 2020, vol. 43, no. B4, pp. 381–390. doi: 10.5194/isprs-archives-XLIII-B4-2020-381-2020.
M. Ahmad and R. Mohd-Mokhtar, “Adaptive Threshold-based Fault Detection for Systems Exposed to Model Uncertainty and Deterministic Disturbance,” Pertanika J. Sci. Technol., vol. 31, no. 6, pp. 3107–3124, 2023, doi: 10.47836/pjst.31.6.26.
A. Abdusalomov et al., “Automatic salient object extraction based on locally adaptive thresholding to generate tactile graphics,” Appl. Sci., vol. 10, no. 10, p. 3350, 2020, doi: 10.3390/app10103350.
C. Liu, F. Xie, X. Dong, H. Gao, and H. Zhang, “Small Target Detection from Infrared Remote Sensing Images Using Local Adaptive Thresholding,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 15, pp. 1941–1952, 2022, doi: 10.1109/JSTARS.2022.3151928.
X. Deng, J. Deng, and Z. Deng, “Adaptive Corner Detection Algorithm Based on OTSU Threshold Segmentation,” in 2024 IEEE 9th International Conference on Computational Intelligence and Applications, ICCIA 2024, 2024, pp. 120–124. doi: 10.1109/ICCIA62557.2024.10719234.
T. Wu, H. Song, H. Gao, Z. Wu, and F. Han, “Adaptive Dynamic Thresholding Method for Fault Detection in Diesel Engine Lubrication Systems,” Machines, vol. 12, no. 12, 2024, doi: 10.3390/machines12120895.
S. Lu, Y. Zhao, and Y. Zhao, “A Motion Object Detection Algorithm Based on Adaptive Thresholding,” 2023. doi: 10.1109/ICSPCC59353.2023.10400265.
W. Kong, J. Chen, Y. Song, Z. Fang, X. Yang, and H. Zhang, “Sobel Edge Detection Algorithm with Adaptive Threshold based on Improved Genetic Algorithm for Image Processing,” 2023. [Online]. Available: http://www.ijacsa.thesai.org
L. Makabe, H. Santo, F. Okura, and Y. Matsushita, “Shape-coded ArUco: Fiducial Marker for Bridging 2D and 3D Modalities,” in Proceedings - 2022 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022, 2022, pp. 2315–2324. doi: 10.1109/WACV51458.2022.00237.
X. T. Hu Xiao-Fei Deng, S. Q. Zhao Qin Guo Cui-Xia Zeng, and K. Q. Zhou, “Comparative Study on Application of Extended Kalman Filter and Unscented Kalman Filter in Target Tracking,” in IOP Conference Series: Materials Science and Engineering, 2020, vol. 864, no. 1. doi: 10.1088/1757-899X/864/1/012055.
Z. Jiang, S. Ma, Z. Li, X. Jiang, J. Chen, and Y. Li, “A Mobile Robot Indoor Positioning System Based on ArUco Array and Extended Kalman Filter,” in Proceedings - 2023 China Automation Congress, CAC 2023, 2023, pp. 562–567. doi: 10.1109/CAC59555.2023.10450326.
H. C. Kam, Y. K. Yu, and K. H. Wong, “An Improvement on ArUco Marker for Pose Tracking Using Kalman Filter Cheun Kam Ho , Ying Kin Yu , and Kin Hong Wong , " An Improvement on ArUco Marker for Pose Tracking Using Kalman Filter ", 19th IEEE / ACIS International Conference on Software Engineering , Artificial Intelligence , Networking and Parallel / Distributed Computing ( SNPD 2018 ) , Novotel An Improvement on ArUco Marker for Pose Tracking Using Kalman Filter,” no. April, pp. 1–6, 2022, doi: 10.1109/SNPD.2018.8441049.
Z. Mu, H. Liu, and S. Zhang, “A Multiple-Step, Randomly Delayed, Robust Cubature Kalman Filter for Spacecraft-Relative Navigation,” Aerospace, 2023, doi: 10.3390/aerospace10030289.
S. Yu, L. Wang, and J. Zhang, “An Improved Unscented Kalman Filtering Combined with Feature Triangle for Head Position Tracking,” Electronics, 2023, doi: 10.3390/electronics12122665.
V. Mkhoyan, J. Ramos, and T. Walker, “Adaptive State Estimation and Real-Time tracking of Aeroelastic Wings with Augmented Kalman filter and Kernelized Correlation Filter,” J. Aerosp. Eng., 2021, doi: 10.2514/6.2021-0666.
D. Klokov, X. Yu, and H. Wang, “A Novel Carrier Tracking Approach for GPS Signals Based on Gauss–Hermite Kalman Filter,” Electronics, 2022, doi: 10.3390/electronics11142215.
M. Bagherzadeh and A. Toosizadeh, “Eye Tracking Algorithm Based on Multi Model Kalman Filter,” Hightech Innov. J., 2022, doi: 10.28991/hij-2022-03-01-02.
A. Babu and C. Eswaran, “Performance Analysis of Path Tracking Using Multi-Dimensional Kalman Filter Based on State Estimations,” J. Control Syst. Eng., 2021, doi: 10.36227/techrxiv.15129843.v1.
Z. Zhuang, X. Li, J. Wang, M. Dai, and S. Ma, “An Adaptive Threshold Detection Circuit for Action Potential,” in Journal of Physics: Conference Series, 2022, vol. 2290, no. 1. doi: 10.1088/1742-6596/2290/1/012003.
R. Berral-Soler, R. Muñoz-Salinas, R. Medina-Carnicer, and M. J. Marín-Jiménez, “DeepArUco++: Improved detection of square fiducial markers in challenging lighting conditions,” Image Vis. Comput., vol. 152, p. 105313, 2024, doi: https://doi.org/10.1016/j.imavis.2024.105313.
M. Tanzib Hosain, A. Zaman, M. R. Abir, S. Akter, S. Mursalin, and S. S. Khan, “Synchronizing Object Detection: Applications, Advancements and Existing Challenges,” IEEE Access, vol. 12, pp. 54129–54167, 2024, doi: 10.1109/ACCESS.2024.3388889.
J. Zhao et al., “An improved unscented kalman filter applied to positioning and navigation of autonomous underwater vehicles,” Sensors, vol. 25, no. 2, p. 551, 2025, doi: 10.3390/s25020551.
T. Liang, K. Li, Y. Wang, and M. Wang, “Optimized adaptive filtering for fault estimation of the dynamics of high-speed train based on robust extended kalman filter,” Trans. Inst. Meas. Control, vol. 45, no. 14, pp. 2688–2711, 2023, doi: 10.1177/01423312231160202.
X. Wang, “Enhancing license plate recognition in low-light conditions using Retinex_FM and YOLO model,” Aerospace, 2025, doi: 10.1117/12.3056113.
J. Liang, Y. Zhang, and X. Li, “Deep Bilateral Retinex for Low-Light Image Enhancement,” arXiv, 2020, doi: 10.48550/arxiv.2007.02018.
Y. Wu, “Enhancing Object Sorting Under Low-Light Conditions with CLAHE, Gaussian Blur, ROI, and Custom PID on a Raspberry Pi Robotic Arm,” Appl. Comput. Eng., 2024, doi: 10.54254/2755-2721/96/20241240.
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