Student | Institute of chemical sciences, Bahauddin zakariya university, Multan | Pakistan
Sumaira Manzoor is an accomplished researcher specializing in robotics, computer vision, and artificial intelligence, with a focus on developing advanced frameworks for autonomous systems and intelligent robots. Her work encompasses the design and deployment of edge-based vision models, human-following robots, and real-time inference systems, emphasizing efficiency and practical applicability in dynamic environments. She has made significant contributions to single and multi-object tracking, face mask recognition, semantic environment modeling, and robotic perception, leveraging both traditional machine vision and modern deep learning techniques. Manzoor has also explored ontology-based knowledge representation for cognitive robotic systems, providing comprehensive surveys and frameworks to enhance robot decision-making and interaction capabilities. Her research includes performance evaluations of cutting-edge detection models such as YOLOv3 and YOLOv4 in real-world applications, and she has developed innovative semantic SLAM and autonomous navigation frameworks aimed at high-level interaction and planning in complex environments. Throughout her career, she has published extensively in high-impact journals and presented her findings at leading international conferences, including Sensors, IEEE Access, Applied Sciences, and the International Conference on Control, Automation, and Systems (ICCAS). Her collaborative work with multidisciplinary teams has advanced the integration of AI-driven perception and reasoning in robotic systems, addressing practical challenges in mobile robotics and human-robot interaction. By bridging theoretical research and applied robotics, Manzoor’s contributions support the development of intelligent systems capable of autonomous decision-making, environment understanding, and adaptive interaction, positioning her as a leading figure in the field of AI-enabled robotics and computer vision. Her research not only advances technical knowledge but also has broad implications for real-world applications, including service robots, surveillance, healthcare, and autonomous navigation technologies.
Profile: ORCID
Featured Publications
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Manzoor, S., An, Y.-C., In, G.-G., Zhang, Y., Kim, S., & Kuc, T.-Y. (2023). SPT: Single pedestrian tracking framework with re-identification-based learning using the Siamese model. Sensors, 23(10), 4906. https://doi.org/10.3390/s23104906
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Manzoor, S., Kim, E.-J., Joo, S.-H., Bae, S.-H., In, G.-G., Joo, K.-J., Choi, J.-H., & Kuc, T.-Y. (2022). Edge deployment framework of GuardBot for optimized face mask recognition with real-time inference using deep learning. IEEE Access. https://doi.org/10.1109/ACCESS.2022.3190538
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Manzoor, S., Joo, S.-H., Rocha, Y. G., Bae, S.-H., Kim, E.-J., Joo, K.-J., & Kuc, T.-Y. (2021). Ontology-based knowledge representation in robotic systems: A survey oriented toward applications. Applied Sciences, 11(10), 4324. https://doi.org/10.3390/app11104324
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Manzoor, S., Joo, S.-H., Rocha, Y. G., Lee, H.-U., & Kuc, T.-Y. (2019). A novel semantic SLAM framework for humanlike high-level interaction and planning in global environment. CEUR Workshop Proceedings.
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Manzoor, S., Joo, S.-H., & Kuc, T.-Y. (2019). Comparison of object recognition approaches using traditional machine vision and modern deep learning techniques for mobile robot. International Conference on Control, Automation and Systems. https://doi.org/10.23919/ICCAS47443.2019.8971680
Sumaira Manzoor’s work advances intelligent robotics and AI-driven perception, enabling autonomous systems to interact safely and efficiently with complex environments. Her research bridges cutting-edge computer vision and real-world applications, driving innovation in robotics, healthcare, surveillance, and autonomous navigation while contributing to global technological progress.