Gopikishan Mahto

Papers

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About

Gopikishan Mahto is a researcher at the intersection of human-computer interaction and embedded computer vision, with a focus on making advanced technology accessible to non-experts. His most cited work, "Real-time gesture control UAV with a low resource framework" (2021, 8 citations), demonstrates a breakthrough in democratizing drone operation. Mahto developed a lightweight system that uses 2D computer vision and deep learning to enable intuitive, gesture-based control of low-cost micro drones equipped only with an onboard RGB camera. This framework eliminates the need for technical expertise or expensive hardware, allowing users to command UAVs through simple hand movements. By proving that complex interaction systems can run efficiently on resource-constrained devices, Mahto's research opens doors for applications in education, emergency response, and assistive technology. His work stands out for its practical emphasis on real-time performance and low computational overhead, making gesture-controlled drones viable for widespread, everyday use. Mahto's contributions highlight a commitment to bridging the gap between cutting-edge AI and user-friendly, inclusive design.