Presented by:
Harley Lara
I like to build stuff 😁
Ilgar Rasulov
Ilgar is a master student at Rhine-Waal University of Applied Sciences(HSRW).
He is busy with research tasks in the field of Data Science, Machine Learning and Artificial Intelligence. Before joining HSRW, he worked for several years as an ERP programmer and database manager for corporate clients.
At HSRW, he is working with mini drones and Jetson computers. There are finished projects of Computer Vision applications, drone control app as well as the versions, that utilise the Jetson Nano/Xavier computer for Computer Vision tasks. He was also in the SNAP! projects, especially in SNAP! - drone, SNAP! - Jetson, and SNAP! - Jetson - drone integraion projects.
He is currently interested in robotic applications, drone software and Computer Vision tasks.
Ilgar Rasulov contributes at 1 Event: Let's plAIy!. at Snap!Con 2022
Rolf Becker
Harley, Ilgar, and Ali are my students who are the creators of our contributions to SNAP!Con 2022. We are presenting together!
Rolf has been a professor for environmental physics at Rhine-Waal University of Applied Sciences (HSRW) since 2010.
He received his diploma in physics from the University of Bonn and his PhD in hydrology from the Karlsruhe Institute of Technology.
At HSRW, he leads the IoT Lab, the Drone Lab, and the Earth Observation Lab (EOLab). In addition to his research and teaching in environmental monitoring in bachelor's and master's programs, he is heavily involved in STEM education. He regularly conducts workshops with schools.
His current interest is combining SNAP! with AI on NVIDIA Jetsons in robotic applications.
Ali Farzizada
Ali is a student at Rhine-Waal University of Applied Sciences (HSRW). He is currently a team member in Earth Observation Lab (EOLab).
Volunteer Hosts
Thanks for helping with Snap!Con 2022!
The talk will follow the process of classifying an image based on Snap! programming, sending it to the Jetson nano to analyze the image and reading it back to Snap for display and interaction, by tracking objects and following objects.
Snap! is used to take video frames from the drone's cam. These frames (pictures) are transmitted to the DetectNet server. This backend object detector reports the bounding box, the object type, and the confidence level for each identified object on the image back to Snap! This information is overlaid with the picture in Snap! This object metadata can be used by Snap! to control Tello minidrone and Jetbot.
- Duration:
- 20 min
- Room:
- Room 3
- Conference:
- Snap!Con 2022
- Type:
- Talk