Slide 1OutlineIntroductionIntroductionBackground - SenseMartBackground - SenseMartSystem DesignSystem DesignSystem Design – Camera SensorSystem Design – Camera SensorSystem Design – Camera SensorSystem Design – Camera SensorSystem Design – Data TransportSystem Design – Central ServerSystem Design – Central ServerComputer Vision AlgorithmComputer Vision AlgorithmComputer Vision AlgorithmFuel Price Board DetectionFuel Price Board DetectionFuel Price Board DetectionFuel Price Board DetectionFuel Price Board DetectionFuel Price Board DetectionFuel Price Board DetectionDimension ComparisonColor Histogram ComparisonColor Histogram ComparisonColor Histogram ComparisonFuel Price ClassificationCharacter ExtractionCharacter RecognitionEvaluationEvaluation - DetectionEvaluation - DetectionEvaluation - ClassificationEvaluation - ClassificationRelated WorkRelated WorkConclusionsReferencesExample GasBuddy.com AppJames PittmanFebruary 9, 2011EEL 6788Automatic Collection of Fuel Prices from a Network of Mobile CamerasA. Dong, S. S. Kanhere, C. T. Chou and N. Bulusu, Automatic Collection of Fuel Prices from a Network of Mobile Cameras, in Proceedings of the 4th IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS), June 2008OutlineIntroductionBackgroundSystem DesignComputer Vision AlgorithmEvaluationRelated WorkConclusions2IntroductionWireless sensor network (WSN) technology has been applied to many different domains◦This paper presents a concept where WSNs are used for collecting consumer pricing information◦The specific target for this paper’s effort is in gathering pricing from fuel (gasoline) stations3IntroductionTwo stations on the same corner of an intersection can have greatly different prices for fuelCurrently websites such as Gaswatch, GasBuddy, and others either:◦Send workers out every day multiple times to collect and track fuel pricing data.◦Rely on input from volunteer site users This is highly labor intensive and inaccurate since stations often update prices at different times of the day4Background - SenseMartThe authors are re-using the Sensing Data Market (SenseMart) framework they proposed in an earlier paperThe SenseMart concept is similar to participatory sensing.◦It leverages existing infrastructure (WSNs) for data collection and encourages the users to share their data to accomplish some high level task.5Background - SenseMartThe SenseMart framework facilitates the data exchange using a “BitTorrent” style concept ◦They incentivize the system by giving a return to the users proportionate to their contributions to encourages data sharing. ◦They did not detail what the ‘return’ was other than access to accurate data on gas prices.6System DesignThe proposed system has 2 methods of operation1. Fuel price collection2. User queryThe first is the focus of this paper.◦Automatic triggering of users phones◦Use of computer vision algorithms + GPS/GIS contextual information to extract the pricing info7System Design8System Design – Camera SensorPrimary function – automatic capture of images of fuel price boards◦Assumed that participating users have cameras mounted in car on dashboard on passenger side (in Australia)◦System could also interface with built in car camera vision systems and transfer data via Wi-Fi or Bluetooth to mobile phones9System Design – Camera SensorA control unit in the mobile phone oversees the capturing operations.◦It periodically polls the GPS receiver to obtain the current location◦A GIS (geographic information system) app such as Google maps or TomTom is required on the phone◦The GIS on the phone is then queried (using GPS location) to gather local contextual information10System Design – Camera Sensor◦If a gas station is known to be close, the control unit estimates viability for image capture (camera facing, distance to target)◦If the situation is deemed viable the camera is activated, images are captured and the camera is deactivated◦The resulting images along with the associated meta-data (location, time of capture, and any GIS data such as station brand) are passed to the “data-upload unit” for upload to the central server11System Design – Camera Sensor12System Design – Data TransportAny data captured by the camera along with the meta-data is transferred to the data upload unit◦This “unit” is generally the ability of the mobile phone to access the internet via 3G or Wi-Fi.◦The device establishes a TCP connection with the server and uploads the data.◦The backup/alternative method is to use multimedia SMS for the data transfer13System Design – Central ServerThe central server stores all of the data and runs the computer vision algorithms.◦Processes the images◦Extracts the fuel pricesThe server also handles the reception of the images, and processing / storing of the associated meta data14System Design – Central ServerThe server processes all of the data in steps1. Detect a fuel board2. Detect the section with the numbers3. Crop the image to the numbers and normalize to a standard size & resolution4. Extract the numbers5. Classify the values6. Report fuel prices15Computer Vision Algorithm16Pictorial overview of the algorithmComputer Vision Algorithm17Challenges to overcome1. Objects obscuring the fuel price boards2. Background color similar / identical to the price boardComputer Vision Algorithm183. Blurred or unfocused image captures (often due to capturing while sensor in motion)4. Sections of the board that share characteristics with the prices (adds, borders)Fuel Price Board DetectionDetecting an fuel board and identifying its location in any given image is challenging◦Authors use GPS and GIS information to reduce the difficulty of the problem◦Each fuel brand has a generally unique color scheme◦Meta – data from GPS/GIS can be used to tag incoming images with fuel brands to guide the system in identifying color information19Fuel Price Board DetectionThere are 2 prominent color schemes for representing images: RGB and HIS (Red-Green-Blue and Hue-Intensity-Saturation)◦HIS is illumination independent but computationally complex◦RGB is illumination sensitive, but computationally efficient◦Authors work with RGB due to targeting mobile applications20Fuel Price Board DetectionRGB is an additive color space, making it easy to extract a single color
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