Park sense

1. Park sense Group #6 Evan Davidson Afsaan Kermani Viker Lamardo Scott Moriarty

2. What is Park Sense? Park sense is a system which maps out a parking lot and detects and displays which parking spaces are vacant and which are occupied.

3. Motivation • We wanted to solve an everyday problem that is relevant to UCF students • Can take up to 30 minutes to find parking during peak traffic hours • 55,000 students at UCF and only 15,500 parking spaces available, not all of which are available to students • Help students and faculty get to class on time

4. Objectives • Accurate car detection • Detect the car during all times of the day • Capable of accommodating a network of cameras so that the project is scalable and can be applied to most of the parking lots in the real world

5. Requirements • Cost effective and easy to install • Weather resistant • Fixed position • Wide range of view • Scheduled day/night mode

6. Specifications • Range: minimum of 2 spaces • Cost: under $10 per parking space • Power source: standard 120VAC (wall outlet) or 12V battery powered • Accuracy: 95% and a system refresh time of less than 20 seconds • Operating environments - • Temperature: 0 to 115 °F • Humidity: 80% RH

7. Features • LCD display that greets the user as they drive in to the parking lot • Website showing live updates on the status of the parking spaces in the parking lot • It doubles as a security system because we are using live video feeds • Parking stats

8. Research

9. Ultrasonic Sensors Advantage Disadvantage • Wide range allowed us to possibly use one sensor per two parking spots • Would require a large number of sensors to cover a full parking lot Ultrasonic Emitter/Receiver 1.64’’

10. Infrared Sensors Advantage Disadvantage • Less expensive than the ultrasonic sensor • Range was not as wide as the ultrasonic sensor Infrared

11. Camera • We decided to use the Vivotek IP7330 for the Park Sense System • Description: bullet-style network camera designed for outdoor applications • Advantages: • shields from harsh conditions such as rain and dust • supports tamper detection (i.e. blockage, redirection, & spray-painting) • Functions as a security camera for students in the parking lot • Has both a long and wide range • Has both day and night vision capabilities • Easy connection router and server to perform image processing • Supports PoE (power over Ethernet) 5.91’’ 2.34’’

12. System Block Diagram Camera Captures an Image Image processing Data analysis Server Generates statistics Outputs data Display

13. Image capture • The network camera captures an jpg image from the mjpg stream

14. Image processing • The captured image is then processed through the code to accurately object detect cars in the parking lot

15. Data output • The program then outputs to a .csv file with information based on the processed image.

16. Dynamic website • The website is able to update the number of rows, columns, and statistical values by reading the .csv file that our initial analysis produces.

17. Display Because we are already generating a webpage, all we have to do is keep that site displayed on an LCD screen mounted at the entrance to the parking lot

18. System refresh & update display • The website is then updated with the proper graphical view of the vacant parking lots.

19. Networking • Originally we planned on using multiple sensors in a mesh network design, but due to budget constraints and design modifications we decided to use a basic Ethernet network to allow for scalability in practical applications • The camera operates on a local area network (LAN) with the computer and display.

20. OpenCV

21. Haar-Like Features • The value of a Haar-like feature is the difference between the sum of the pixel gray values within the black and white rectangular regions • The image is then scanned by a sub window containing a Haar-like feature trying to detect on the classifiers it was trained on

22. Cascade of Classifiers • AdaBoost uses a “weak” learning algorithm and a training set to create strong classifiers • Adaptive because later classifiers are geared to be in favor of sub-windows misclassified by previous classifiers • Series of classifiers are applied to every sub-window, the first eliminates a large number of negative sub-windows and passes all positives (lots of false positives) • Followed by a set amount of stages which do the same to reduce the number of false positives in the final product

23. Data Preparation • Collect positive images. • Obtain negative sample set. • Create info files. • Generate vec files. • Train cascade.

24. Object Marking • For the positive sample set, object marking was required. The positive info file looked like the following:

25. Testing a Cascade

26. Software • C++ • OpenCV Library • Single Run, repeated using a batch file • Can be continual • Low CPU usage • Scalable

27. Software Flow • Capture image • Gray scale • Object detection • Region of interest rectangle comparing • Output to .csv file • Sleep for set time interval repeat

28. Determining a Hit • For each rectangular object detected, the X,Y coordinates and the height and width are stored in a cvRect() object within another object which contains the total number of objects found, CvSeq • These are passed to the function RectDetect() which also takes in the same set of values for a single ROI • The boundaries of each are compared to determine there’s an overlap • Once a hit is announced for a given ROI no other detected objects will be compared to that ROI to save on processing time

29. Testing

30. Testing • Haar training: • Created a variety of cascades including cars, headlights and windshields. Also tested rectangles to possibly detect empty spots. • Testing the cascades • Analyzing hits, misses, and false hits • Website: • Loading the .csv properly • Aligning .csv cells with parking spots

31. The Test Setup • Determine best test subjects among model cars or pictures of cars • Determine the best camera positioning for accuracy. • Control the lighting • Testing IR • Calibrate the regions of interest

32. Testing Results • Trying to detect full cars and headlights, resulted in ~ 0% • We tried to detect windshields on a cascade using ~500 positives and ~500 negatives. • Results varied between 40-80% • Shadows generate false positives. • Camera distance from the cars affected accuracy • Nuanced lighting led to diminished accuracy • IR detection worked toward center of image.

33. Testing Results Continued • The windshields likely succeeded because it resembles a Haar-like feature. Line features and center surround features. • The windshield is somewhat consistent between all cars. • Ideally, multiple cascades would be used to detect all cars.

34. Problems • We are all hardware oriented in a mostly software project • Lighting conditions in the parking lot were a problem as stated. • Cars that are the same color as the asphalt. • Working with open source library was difficult. • Recommended cascades call for between 2,000 and 4,000 positives and 4,000 to 10,000 negatives. • Training of such a robust cascade would require at least a week of processing on a home computer

35. Budget

36. Milestones

37. Demo andQ & A