Sabir Hossain

About

Sabir Hossain is a robotics and artificial intelligence researcher whose work bridges the gap between autonomous systems and real-world deployment. His primary research areas include deep learning for computer vision, autonomous navigation, and embedded AI systems for robotics. Hossain’s most impactful contribution is his pioneering work on real-time multiple-object detection and tracking from aerial imagery using GPU-equipped drones. His 2019 paper on this topic has garnered 180 citations, demonstrating its significance in enabling drones to process complex visual data onboard for applications like surveillance and search-and-rescue. Additionally, he has advanced the field of autonomous ground vehicles through deep reinforcement learning, as shown in his 2020 work on a ROS-controlled RC car that explores unknown environments using LiDAR data. This research, while newer with 13 citations, highlights his commitment to scalable, cost-effective solutions for robot navigation. Hossain’s work is notable for its practical integration of cutting-edge AI with embedded hardware, making autonomous systems more accessible and efficient. His contributions continue to inspire researchers in robotics and AI.

Research Focus

Key Achievements

Top Papers

1. 1
   Deep Learning-Based Real-Time Multiple-Object Detection and Tracking from Aerial Imagery via a Flying Robot with GPU-Based Embedded Devices

   180 citations · 2019
2. 2
   Deep Reinforcement Learning-based ROS-Controlled RC Car for Autonomous Path Exploration in the Unknown Environment

   13 citations · 2020

Key Collaborators

Contact & Links