Muazzam Maqsood
Papers
2
Total Citations
62
H-Index
2
About
Muazzam Maqsood is a leading researcher in computer vision and intelligent systems, with a core focus on autonomous navigation and human action recognition. His work addresses critical challenges in real-world AI deployment, particularly in safety-critical applications. Maqsood’s highly cited 2021 study, “A transfer learning-based efficient spatiotemporal human action recognition framework for long and overlapping action classes” (48 citations), introduced a novel deep learning architecture that significantly improves the accuracy of recognizing complex, sequential human movements—a breakthrough for surveillance and human-computer interaction. He further advanced autonomous vehicle safety with his 2022 paper on “Small obstacles detection on roads scenes using semantic segmentation for the safe navigation of autonomous vehicles” (14 citations), which tackles the urgent problem of detecting small road hazards that often lead to severe accidents. By combining transfer learning with semantic segmentation, Maqsood’s work enables more reliable perception in dynamic environments. His contributions are shaping the next generation of robust, real-time vision systems, making him a key figure in bridging the gap between academic research and practical, life-saving technologies.
Research Focus
Key Achievements
Top Papers
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