CAMShift Algorithm for Human Tracking in the Collaborative Robot Working Area
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Keywords

Industry 5.0
Collaborative Robots
Work Area, Computer Vision,
CAMShift Algorithm, Tracking People

How to Cite

CAMShift Algorithm for Human Tracking in the Collaborative Robot Working Area. (2024). Journal of Universal Science Research, 2(8), 87-101. https://universalpublishings.com/index.php/jusr/article/view/6881

Abstract

This article considers the complex implementation of the CAMShift algorithm for human tracking in the collaborative robot working area. The study covers both the algorithmic and mathematical underpinnings of CAMShift, detailing the underlying principles and mathematical models used to improve tracking accuracy. A Python program was developed in the PyCharm environment to effectively implement this algorithm, taking into account aspects such as real-time processing and integration with robotic systems. The research conducted a comprehensive assessment of the tracking speed, studied how effectively the algorithm works in different conditions and how it affects the overall sensitivity of the system. The results demonstrate the effectiveness of the CAMShift algorithm in providing accurate and timely tracking, highlighting its suitability for dynamic and interactive environments. This work helps to optimize the performance of collaborative robots by improving tracking capabilities, enabling better interaction and safety in shared work areas.

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DOI

References

Nevliudov, I., Yevsieiev, V., Baker, J. H., Ahmad, M. A., & Lyashenko, V. (2020). Development of a cyber design modeling declarative Language for cyber physical production systems. J. Math. Comput. Sci., 11(1), 520-542.

Abu-Jassar, A. T., Al-Sharo, Y. M., Lyashenko, V., & Sotnik, S. (2021). Some Features of Classifiers Implementation for Object Recognition in Specialized Computer systems. TEM Journal: Technology, Education, Management, Informatics, 10(4), 1645-1654.

Al-Sharo, Y. M., Abu-Jassar, A. T., Sotnik, S., & Lyashenko, V. (2021). Neural networks as a tool for pattern recognition of fasteners. International Journal of Engineering Trends and Technology, 69(10), 151-160.

Matarneh R., & et al. (2017). Speech Recognition Systems: A Comparative Review. Journal of Computer Engineering (IOSR-JCE), 19(5), 71–79.

Nevliudov, I., Yevsieiev, V., Lyashenko, V., & Ahmad, M. A. (2021). GUI Elements and Windows Form Formalization Parameters and Events Method to Automate the Process of Additive Cyber-Design CPPS Development. Advances in Dynamical Systems and Applications, 16(2), 441-455.

Mustafa, S. K., Yevsieiev, V., Nevliudov, I., & Lyashenko, V. (2022). HMI Development Automation with GUI Elements for Object-Oriented Programming Languages Implementation. SSRG International Journal of Engineering Trends and Technology, 70(1), 139-145.

Yevsieiev, V., & et al. (2024). The Canny Algorithm Implementation for Obtaining the Object Contour in a Mobile Robot’s Workspace in Real Time. Journal of Universal Science Research, 2(3), 7–19.

Abu-Jassar, A., & et al. (2024). The Optical Flow Method and Graham’s Algorithm Implementation Features for Searching for the Object Contour in the Mobile Robot’s Workspace. Journal of Universal Science Research, 2(3), 64-75.

Yevsieiev, V., & et al. (2024). The Sobel algorithm implementation for detection an object contour in the mobile robot’s workspace in real time. Technical Science Research in Uzbekistan, 2(3), 23-33.

Nevliudov, I., & et al. (2023). Mobile Robot Navigation System Based on Ultrasonic Sensors. In 2023 IEEE XXVIII International Seminar/Workshop on Direct and Inverse Problems of Electromagnetic and Acoustic Wave Theory (DIPED), IEEE, 1, 247-251.

Yevsieiev, V., & et al. (2024). Object Recognition and Tracking Method in the Mobile Robot’s Workspace in Real Time. Technical science research in Uzbekistan, 2(2), 115-124.

Samoilenko, H., & et al. (2024). Review for Collective Problem-Solving by a Group of Robots. Journal of Universal Science Research, 2(6), 7-16.

Nikitin, V., & et al. (2023). Traffic Signs Recognition System Development. Multidisciplinary Journal of Science and Technology, 3(3), 235-242.

Nevliudov, I. S., & et al. (2023). Conveyor Belt Object Identification: Mathematical, Algorithmic, and Software Support. Appl. Math. Inf. Sci. 17, 6, 1073-1088.

Maksymova, S., & et al. (2024). The Lucas-Kanade method implementation for estimating the objects movement in the mobile robot’s workspace. Journal of Universal Science Research, 2(3), 187-197.

Yevsieiev, V., & et al. (2024). Building a traffic route taking into account obstacles based on the A-star algorithm using the python language. Technical Science Research In Uzbekistan, 2(3), 103-112.

Abu-Jassar, A., & et al. (2023). Obstacle Avoidance Sensors: A Brief Overview. Multidisciplinary Journal of Science and Technology, 3(5), 4-10.

Deineko, Zh., & et al.. (2021). Color space image as a factor in the choice of its processing technology. Abstracts of I International scientific-practical conference «Problems of modern science and practice» (September 21-24, 2021). Boston, USA, pp. 389-394.

Putyatin, Y. P., & et al.. (2016) The Pre-Processing of Images Technique for the Material Samples in the Study of Natural Polymer Composites. American Journal of Engineering Research, 5(8), 221-226.

Lyashenko, V., & et al.. (2021). Wavelet ideology as a universal tool for data processing and analysis: some application examples. International Journal of Academic Information Systems Research (IJAISR), 5(9), 25-30.

Kobylin, O., & Lyashenko, V. (2014). Comparison of standard image edge detection techniques and of method based on wavelet transform. International Journal, 2(8), 572-580.

Rabotiahov, A., Kobylin, O., Dudar, Z., & Lyashenko, V. (2018, February). Bionic image segmentation of cytology samples method. In 2018 14th International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering (TCSET) (pp. 665-670). IEEE.

Baker, J. H., Laariedh, F., Ahmad, M. A., Lyashenko, V., Sotnik, S., & Mustafa, S. K. (2021). Some interesting features of semantic model in Robotic Science. SSRG International Journal of Engineering Trends and Technology, 69(7), 38-44.

Lyashenko, V., Kobylin, O., & Ahmad, M. A. (2014). General methodology for implementation of image normalization procedure using its wavelet transform. International Journal of Science and Research (IJSR), 3(11), 2870-2877.

Гиренко, А. В., Ляшенко, В. В., Машталир, В. П., & Путятин, Е. П. (1996). Методы корреляционного обнаружения объектов. Харьков: АО “БизнесИнформ, 112.

Babker, A. M., Abd Elgadir, A. A., Tvoroshenko, I., & Lyashenko, V. (2019). Information technologies of the processing of the spaces of the states of a complex biophysical object in the intellectual medical system health. International Journal of Advanced Trends in Computer Science and Engineering, 8(6), 3221-3227.

Vasiurenko, O., Lyashenko, V., Baranova, V., & Deineko, Z. (2020). Spatial-Temporal Analysis the Dynamics of Changes on the Foreign Exchange Market: an Empirical Estimates from Ukraine. Journal of Asian Multicultural Research for Economy and Management Study, 1(2), 1-6.

Mustafa, S. K., Ayaz, A. M., Baranova, V., Deineko, Z., Lyashenko, V., & Oyouni, A. A. A. (2020). Using wavelet analysis to assess the impact of COVID-19 on changes in the price of basic energy resources. International Journal of Emerging Trends in Engineering Research, 8(7), 2907-2912.

Matarneh, R., Tvoroshenko, I., & Lyashenko, V. (2019). Improving Fuzzy Network Models For the Analysis of Dynamic Interacting Processes in the State Space. International Journal of Recent Technology and Engineering, 8(4), 1687-1693.

Khan, A., Joshi, S., Ahmad, M. A., & Lyashenko, V. (2015). Some effect of Chemical treatment by Ferric Nitrate salts on the structure and morphology of Coir Fibre Composites. Advances in Materials Physics and Chemistry, 5(1), 39-45.

Kuzemin, O., & Lyashenko, V. Microsituation Concept in GMES Decision Support Systems/A. Kuzemin, V. Lуashenko. Intelligent Data Processing in Global Monitoring for Environment and Security (pр. 217–238).–2011.–Р, 217-238.

Lyashenko, V., Matarneh, R., & Kobylin, O. (2016). Contrast modification as a tool to study the structure of blood components. Journal of Environmental Science, Computer Science and Engineering & Technology, 5(3), 150-160.

Lyashenko V., & et al. (2023). Automated Monitoring and Visualization System in Production. Int. Res. J. Multidiscip. Technovation, 5(6), 09-18.

Lyubchenko, V., & et al.. (2016). Digital image processing techniques for detection and diagnosis of fish diseases. International Journal of Advanced Research in Computer Science and Software Engineering, 6(7), 79-83.

Lyashenko, V. V., Matarneh, R., Kobylin, O., & Putyatin, Y. P. (2016). Contour Detection and Allocation for Cytological Images Using Wavelet Analysis Methodology. International Journal, 4(1), 85-94.

Sotnik, S., & Lyashenko, V. (2022). Prospects for Introduction of Robotics in Service. Prospects, 6(5), 4-9.

Al-Sharo Y., & et al. (2023). A Robo-hand prototype design gripping device within the framework of sustainable development. Indian Journal of Engineering, 20, e37ije1673.

Abu-Jassar, A. T., Attar, H., Lyashenko, V., Amer, A., Sotnik, S., & Solyman, A. (2023). Access control to robotic systems based on biometric: the generalized model and its practical implementation. International Journal of Intelligent Engineering and Systems, 16(5), 313-328.

Al-Sharo, Y. M., Abu-Jassar, A. T., Sotnik, S., & Lyashenko, V. (2023). Generalized Procedure for Determining the Collision-Free Trajectory for a Robotic Arm. Tikrit Journal of Engineering Sciences, 30(2), 142-151.

Terreran, M., & et al. (2020). Low-cost scalable people tracking system for human-robot collaboration in industrial environment. Procedia Manufacturing, 51, 116-124.

Zaccaria, M., & et al. (2021). Multi-robot multiple camera people detection and tracking in automated warehouses. In 2021 IEEE 19th International Conference on Industrial Informatics (INDIN), IEEE, 1-6.

Sambolek, S., & Ivasic-Kos, M. (2021). Automatic person detection in search and rescue operations using deep CNN detectors. Ieee Access, 9, 37905-37922.

Koide, K., & et al. (2020). Monocular person tracking and identification with on-line deep feature selection for person following robots. Robotics and Autonomous Systems, 124, 103348.

Müller, S., & et al. (2020). A multi-modal person perception framework for socially interactive mobile service robots. Sensors, 20(3), 722.

Vendrow, E., & et al. (2023). Jrdb-pose: A large-scale dataset for multi-person pose estimation and tracking. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 4811-4820).

Eppenberger, T., & et al. (2020). Leveraging stereo-camera data for real-time dynamic obstacle detection and tracking. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 10528-10535.

De Langis, K., & Sattar, J. (2020). Realtime multi-diver tracking and re-identification for underwater human-robot collaboration. In 2020 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 11140-11146.

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