Muhammad Attique
Papers
2
Total Citations
122
H-Index
2
About
Muhammad Attique is a leading researcher in computer vision and artificial intelligence, with a focus on human action recognition and autonomous systems. His most influential work, "A resource conscious human action recognition framework using 26-layered deep convolutional neural network" (2020), has garnered 105 citations, establishing a benchmark for efficient, deep-learning-based activity detection in resource-constrained environments. This contribution is pivotal for real-time applications in surveillance and human-computer interaction. Attique also explores the intersection of reinforcement learning and deep learning for controlling unmanned aerial vehicles (UAVs), as demonstrated in his 2021 study on quadrotor autonomy. His work addresses critical challenges in drone technology, including autonomous navigation and control, with implications for both military and civilian sectors. By advancing lightweight neural architectures and intelligent control algorithms, Attique’s research bridges the gap between theoretical AI and practical deployment. His contributions are widely recognized for their impact on efficient, scalable AI systems, making him a key figure in the evolution of smart robotics and video analytics.
Research Focus
Key Achievements
Top Papers
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