1. Automated Parking Lot Attendant SDP ’07 Team Frasier 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 This is a bad lot Many obstacles Parking spaces are obscured Hard to map

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

6. However ► Still things we need to worry about

7. The Camera ► Axis 210 Network Camera ► Set up on 2 nd 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 Wait for timer to expire Query camera, grab snapshot Save snapshot locally with unique filename Send image data to Matlab for image processing Matlab returns processing results Display to user Initialize system

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

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.. t 0 …… t 5 ….. t 10 … ► 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 2 (Picture 1) – (Picture 2) Picture 1

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?