Leveraging Smartphone Cameras for Collaborative Road Advisories
ABSTRACT:
Ubiquitous
smartphones are increasingly becoming the dominant platform for collaborative
sensing. Smartphones, with their ever richer set of sensors, are being used to
enable collaborative driver-assistance services like traffic advisory and road
condition monitoring. To enable such services, the smartphones’ GPS,
accelerometer, and gyro sensors have been widely used. On the contrary,
smartphone cameras, despite being very powerful sensors, have largely been
neglected. In this paper, we introduce a collaborative sensing platform that
exploits the cameras of windshield-mounted smartphones. To demonstrate the
potential of this platform, we propose several services that it can support,
and prototype SignalGuru, a novel service that leverages wind shield mounted smartphones
and their cameras to collaboratively detect and predict the schedule of traffic
signals, enabling Green Light Optimal Speed Advisory (GLOSA) and other novel
applications. Results from two deployments of SignalGuru, using iPhones in cars
in Cambridge (MA, USA) and Singapore, show that traffic signal schedules can be
predicted accurately. On average, SignalGuru comes within 0.66 s, for pretimed
traffic signals and within 2.45 s, for traffic-adaptive traffic signals.
Feeding SignalGuru’s predicted traffic schedule to our GLOSA application, our
vehicle fuel consumption measurements show savings of 20.3 percent, on average.
EXISTING SYSTEM:
We show that
accurate and near real-time camera-based sensing is possible. Many drivers are
already placing their phones on the windshield in order to use existing popular
services like navigation. Once a phone is placed on the windshield, its camera
faces the road traffic signals are deployed only in a few cities around the
world. The cost of updating existing traffic signals to include such timers has
hindered their widespread deployment.
The color and size
of the bulb, a specific area around the bulb is checked for the existence of a
horizontal or vertical black box, the traffic signal housing.
PROPOSED SYSTEM:
Information from
GPS, accelerometer and proximity sensors in order to estimate traffic
conditions detect road abnormalities, collect information for available parking
spots and compute fuel efficient routes. Our camera-based traffic signal
detection algorithm is drawn from several schemes mentioned above. In contrast
to these approaches that detect asingle target, SignalGuru uses an iterative
threshold-based approach for identifying valid traffic signal candidates.
MODULE:
Intelligent Transportation
US and European
transportation agencies recognize the importance of GLOSA and access to traffic
signal schedules, and thus have advocated for the integration of short-range
(DSRC) antennas into traffic signals as part of their longterm vision. Traffic
signal settings can be acquired from city transportation authorities. In case
they are not available, the settings (phase lengths) can be measured as
described
Adaptive Traffic Signals
To predict
traffic-adaptive traffic signals, information from all phases (intersecting
roads) of an intersection is needed. more collaborating nodes and more traffic
signal history can improve the prediction accuracy for the challenging
traffic-adaptive traffic signals. the schedule of the traffic signals ahead,
nodes need either the database of the traffic signal settings (for pre timed
traffic signals) or the Support Vector Regression prediction models (for
traffic-adaptive signals)
Traffic Signal Detection
In Signal Guru,
traffic signal detection is the most compute-intensive task. A traffic signal detection algorithm that runs on
resource constrained smart phones must be lightweight so that video frames can
still be processed at high frequencies. Uncontrolled environment composition
and false detections: Windshield-mounted smart phones capture the real world
while moving. As a result, there is no control over the composition of the
content captured by their video cameras.
Color Filtering
The first step of
the detection algorithm is the color filtering process, as the most distinctive
feature of traffic signals is the bright color of their bulbs. The color filter
inspects the color of all pixels of an image (video frame) and zeroes out the
pixels that could not belong to a red, yellow, or green traffic signal bulb.
After color filtering, only objects that have the correct color are maintained
in the image. The next stages examine which of them qualify to be a traffic
signal based on their shape
HARDWARE REQUIREMENTS:-
•
System :
Pentium IV 2.4 GHz.
•
Hard Disk :
40 GB.
•
Floppy Drive :
1.44 Mb.
•
Monitor : 15 VGA Colour.
•
Mouse :
Logitech.
•
RAM :
256 Mb.
Software Requirements:
•
Operating
system : - Windows XP
Professional.
•
Front
End :
- Visual Studio.Net 2008
•
Coding
Language : - Visual C# .Net.
REFERENCE:
Emmanouil
Koukoumidis, Student Member, IEEE, Margaret Martonosi, Fellow, IEEE, and
Li-Shiuan Peh, Member, IEEE, Leveraging Smartphone Cameras for Collaborative
Road Advisories”, IEEE TRANSACTIONS ON
MOBILE COMPUTING, VOL. 11, NO. 5, MAY 2012
