Io Tracker Homemade Tracking System using Coresets and

Io. Tracker: Home-made Tracking System using Core-sets and the Internet of (Tracking) Things Soliman Nasser Ibrahim Jubran Artem Barger Dan Feldman Robotics and Big Data Lab, Department of Computer Science , University of Haifa Io. Tracker System Overview The hardware of our system consists of the following commercial products: Problem Statement Our Approuch • Web camera: A ~10$ camera, used for tracking, both in IR and RGB modes. • Client’s board: Odroid U 3, or Intel’s Galileo Gen 2 (30$-50$). Each camera is connected to its own board using a USB cable. Each board is equipped with a wifi adapter for communication to the server. • Server’s board (same as Camera’s board). This is the single board that collects the tracking information from all the other cameras through the UDP based communication protocol that we implemented. Figure 1: Io. Tracker clients installed near the ceiling of Jacob’s building, University of Haifa. Motivation Motion Tracking System. Motion tracking systems can be used for indoor localization, navigation, and robot controlling. Applications: • Guardian Angel: navigation people inside buildings. (malls, hospitals, etc. ) • Autonomous drones and vehicles. • Smart Homes. Existing Systems. Existing motion capture systems for such application use dedicated hardware and workstations that cost thousands of dollars, and thus exist mainly is research labs. Our system will give this ability to non-experts that only have a limited budget. Figure 3: System overview, Left: server side, Right: client side Camera Hardware Challenge We needed a fast and simple detection and tracking algorithm, therefore we disassembled and modified the camera lens in order to capture only IR wavelengths. RESEARCH POSTER PRESENTATION DESIGN © 2012 www. Poster. Presentations. com References [1] Lepetit, Vincent, Francesc Moreno-Noguer, and Pascal Fua. "Epnp: An accurate o (n) solution to the pnp problem. " International journal of computer vision 81. 2 (2009)” Figure 4: Left: A picture from a pre-modified camera, Right: A picture from a modified camera, capturing only IR wavelengths Figure 2: Autonomous mini quadcopter hovering in Jacob’s building. [2] S. Nasser, A. Barry, M. Doniec, G. Peled, G. Rosman, D. Rus, M. Volkov, D. Feldman. Fleye on the Car: Big Data meets the Internet of Things. ” ACM 14 th International Conference on Information Processing in Sensor Networks (IPSN '15)” Figure 6: Features selected, on the robot, for the Pn. P algorithm [3] D. Feldman, M. Langberg. A Unified Framework for Approximating and Clustering Data. “ACM Symposium on Theory of Computing (STOC 2011)”