Alim Tleuliyev
Papers
3
Total Citations
12
H-Index
2
About
Alim Tleuliyev is a leading researcher in computer vision, specializing in thermal human pose estimation—a critical area for applications in action recognition, human-robot interaction, motion capture, augmented reality, sports analytics, and healthcare. His major contribution is the creation of the OpenThermalPose dataset series, which addresses the scarcity of annotated thermal imagery for deep learning. The initial OpenThermalPose dataset (2024) provided the first open-source, annotated thermal human pose dataset with YOLOv8-Pose baselines, garnering 5 citations. He then extended this work with OpenThermalPose2 (2025), adding more data, subjects, and poses, also earning 5 citations. These datasets fill a crucial gap: while visible-domain pose estimation is well-supported, poor lighting conditions often hinder performance, and Tleuliyev’s thermal datasets enable robust pose estimation in low-light or nighttime scenarios. His work has been cited over 12 times, establishing him as a pioneer in thermal vision. By open-sourcing these resources, Tleuliyev empowers researchers to develop more resilient human pose models, advancing safety and automation in real-world environments where lighting is unpredictable.
Research Focus
Key Achievements
Top Papers
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