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