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Automated Parking Lot Attendant

SDP ’07 Team Frasier. Automated Parking Lot Attendant. Tom Cleary Matt Regan. Bill Ryan Adam Bailin. Current System. Disorderly Confusing Antiquated. Large Parking Lots. The larger the parking lot, the more difficult it is to find a parking space. Choosing a Lot. Many obstacles

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Automated Parking Lot Attendant

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  1. SDP ’07 Team Frasier Automated Parking Lot Attendant Tom Cleary Matt Regan Bill Ryan Adam Bailin

  2. Current System • Disorderly • Confusing • Antiquated

  3. Large Parking Lots • The larger the parking lot, the more difficult it is to find a parking space

  4. Choosing a Lot • Many obstacles • Parking spaces are obscured • Hard to map This is a bad lot

  5. The lot we chose • Fewer obstacles • Parking spaces easily identifiable • Easier to map This is a good lot

  6. However • Still things we need to worry about

  7. The Camera Axis 210 Network Camera Set up on 2nd Floor KEB

  8. System Overview Project all about image processing Two main parts: control and processing Need a central way to control system Basic steps of control system 1) Take picture 2) Send to Matlab 3) Receive from Matlab 4) Display to user

  9. Controlling the System Block diagram for control system Generate readable result Initialize system Wait for timer to expire Query camera, grab snapshot Display to user Save snapshot locally with unique filename Send image data to Matlab for image processing Matlab returns processing results

  10. Take picture every 3 seconds using Timer Run m-file from Matlab Wait for Matlab to return results Arrange results in human-readable form Create image – layout of parking lot with indication as to which spots are taken Controlling the System

  11. Software Using Microsoft’s .NET framework Classes WebRequest() - request web resource (image.jpg) HttpWebResponse() – returns jpg data stream FileStream() – saves stream locally Timer() - take pictures at interval

  12. User Interface • Will present user with computer generated map of parking lot

  13. Problems and Solutions Learning curve for Visual Studio and MATLAB Network congestion (wireless vs. wired) .jpg image size (640x480) Delays to/from Matlab

  14. Must read picture into Matlab “imread(‘c:\snapshot.jpg’)” Image is 3-dimensional(red, green, blue) Snapshot 480x640x3 uint8 We have our picture on file, now what?

  15. Our image Processing Basic idea: Image Differencing! Is the new snapshot different from the base snapshot? If so, something must have changed Cut large snapshot into smaller pieces Each small piece is of one parking spot Pixels are manually mapped to each spot All processing done on small pictures individually

  16. Scaling Example • This is one example of pixel mapping • Most processing will be done on these small pictures

  17. How Different? No two pictures are alike Glare, shadows, random ambiences. How different are two pictures? Correlation coefficient! Variable which represents how different or alike two pictures are Between -1 and 1, 1 being two identical pictures A correlation coefficient below the threshold causes concern! State of parking spot is changed New snapshot becomes the base

  18. A visual.. t0…… t5….. t10… • A visual of how the program will run

  19. Differencing Issues Ambience's blocking camera position What if a truck blocks the view? Solution! Timing buffer The base picture is only changed if the new picture is different for a time Something that is blocking the camera will likely move away

  20. More Issues… Cars aren’t the only thing that can cause a change Daylight gradually changes the new snapshot from the base Solution! Use full snapshot A subtraction will show where the most change took place

  21. Determine Ambient Conditions Look at area of just pavement If average of pixels is similar, spot is probably empty

  22. MDR Specifications Mount camera in good location overlooking a lot near Knowles Engineering Building and connect to network Able to import an image into an image processing program Able to manipulate an image using basic image processing techniques

  23. Live view of camera http://abyss.ecs.umass.edu:8080

  24. Images RGB Grayscale Edge Detection

  25. Images Picture 1 Picture 2 (Picture 1) – (Picture 2)

  26. Looking ahead… Need to explore the effects of weather conditions such as rain and snow May need to consider alternate image processing solutions due to the following observations: Pixel subtraction may not be accurate based on time of day Obstructions (groups of people, cars driving through parking lot) Glare on window directly in front of camera – solved with box Have many ways of determining spots – can average them, have threshold for ‘spot taken’ event We’re over the learning curve Our demo

  27. Questions?

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