IoTracker:
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
IoTracker
A low-cost motion tracking system for indoor localization,
based on web-cameras connected to “Internet of Things”
(IoT) boards.
• 40 FPS real time tracking.
• Full 6DOF (𝑋, 𝑌, 𝑍, 𝜑,𝜃,𝛹)
• Low-cost hardware.
• Easy to deploy.
• Mobility.
System Overview
The hardware of our system consists of the following
commercial products:
• Web camera: A ~10$ camera, used for tracking,
both in IR and RGB modes.
• Client’s board: Odroid U3, or Intel’s Galileo
Gen2 (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.
Problem Statement
Perspective-n-Point:
The aim of the Perspective-n-point problem is to determine the
position and orientation of a camera given its intrinsic parameters
and a set of n correspondences between 3D points 3D lines.
Our Approuch
Coreset for PnP:
Definition:
An 𝜺-coreset for PnP is a set 𝑆 ⊆ 1,2, … , n of 𝑆 indexes and
𝑆 weights 𝑤1 , 𝑤2 , … , 𝑤𝑛 such that: 𝑂𝑃𝑇 𝑃, 𝐿 is approximated by
min 𝑖∈𝑆 𝑤𝑖 ∗ dist 2 (𝑇 𝑝𝑖 , 𝑙𝑖 ) up to (1-𝜀) multiplicative factor.
𝑇∈𝑇
Formal Definition of the PnP:
Let P = {𝑝1 , 𝑝2 , … , 𝑝𝑛 } be a set of n points, and 𝐿 = {𝑙1 , 𝑙2 , … , 𝑙𝑛 }
be a set of n lines, both in 𝑅3 .
Compute a translation and rotation 𝑇 over every rotation and
translation 𝑇 in 𝑅3 , that minimizes: OPT P, L :
= 𝑛𝑖=1 dist 2 (𝑇 𝑝𝑖 , 𝑙𝑖 ), where dist 2 𝑝, 𝑙 is the squared distance
from a point 𝑝 to the line 𝑙.
Figure 7: A set of 3D point (P), and a subset of points
{𝒄𝟏 , 𝒄𝟐 , 𝒄𝟑 , 𝒄𝟒 } ∈ 𝑷 which are the coreset of P
Figure 1: IoTracker 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 5: 5 pairs of points and lines correspondences in 𝑹𝟑 , from
which we calculate the translation and rotation matrices.
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.
Streaming version of the PnP:
Definition:
Update 𝑂𝑃𝑇(𝑃, 𝐿) after insertion / deletion of a pair (𝑝𝑖 , 𝑙𝑖 ) of
point 𝑝𝑖 in 𝑃 and it’s corresponding line 𝑙𝑖 in 𝐿 using 𝑂 logn time
and memory per pair.
Our research challenge is to prove the following theorem:
For every 𝜀 > 0 there is an 𝜺-coreset (𝑆, 𝑊) for (𝑃, 𝐿) of size
logn
𝑆 =𝑂
. This coreset can be updated in 𝑆 time per
ε
insertion / deletion of a pair 𝑝𝑖 , 𝑙𝑖 of point 𝑝𝑖 and line 𝑙𝑖
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)”
[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 14th International Conference
on Information Processing in Sensor Networks (IPSN '15)”
Figure 4: Left: A picture from a pre-modified camera, Right: A picture
from a modified camera, capturing only IR wavelengths
Figure 6: Features selected, on the robot, for the PnP algorithm
[3] D.Feldman, M.Langberg. A Unified Framework for
Approximating and Clustering Data. “ACM Symposium on
Theory of Computing (STOC 2011)”
Figure 2: Autonomous mini quadcopter hovering in Jacob’s building.
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