1. Vision-based Android Application for GPS Assistance in Tunnels
The Andrew & Erna Viterbi Faculty of
Electrical Engineering
Electronics
Computers
Communications
Vision-based Android Application for GPS Assistance in Tunnels
Lior Carmi, Shirin Manzur, Supervisor: Amit Aides
The Problem
GPS navigation with smartphone (‘WAZE’)
Signal loss in tunnels
Late or missing navigation directions
Test case: Carmel Tunnels
Road Sign Recognition
Rectangle Recognition 1 2 3 .. 12
other
SVM Classifier ?
Final Algorithm
Initial velocity & location
Road Mark Recognition
Kalman Filter
Suggested Solution
Utilize camera to estimate location
Feature point tracking: Tested using PTAM algorithm Fails due to fast changing video frames
Tunnel light tracking: Easy to recognize / no occlusions / known distances
Recognition of known infrastructure: Road signs every 250 meter
Evaluate usefulness of motion sensors: Useful for small distances only
Estimated speed and location
Absolute location
Speed Samples (from Light Recognition)
0.1 sec
Last location
Last velocity
Kalman Filter
State System
Update Prediction
x(k+1|k)
x(k+1)
x(k)
z(k+1)
Current state
Predict next state
Estimate next state
Measurement
Workflow
Exploration of existing solutions
Computer simulations
Algorithm development
Real-time implementation
Application development
PTAM
Results
Tunnel Light Tracking
Results
ROI & Convert to binary (dynamic threshold)
Real-time Android Application
Computer Simulation
Estimation Error at tunnel exit --
15 m ( < 0.5% error)
Both methods *
45 m ( < 2% error)
30 m ( < 1% error)
Light Tracking only
Shape based filtering, (morphological operations)
Classification of objects (linear series of 3 lights)
* Both methods = Light tracking & Road sign recognition
Summery
Real-time Android application
Successfully tested on test-case
Easy adjustment to any tunnel
Easy integration with WAZE or similar
Demo
Detect new light (10% of top of frame)
Calculate speed
LampsDelta=17 [m]
FPS=10 [frames/sec]
Distinguished Project Award in EE Project Contest June 2015