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Park sense. Group #6 Evan Davidson Afsaan Kermani Viker Lamardo Scott Moriarty. 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. Motivation.
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Park sense Group #6 Evan Davidson Afsaan Kermani Viker Lamardo Scott Moriarty
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.
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
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
Requirements • Cost effective and easy to install • Weather resistant • Fixed position • Wide range of view • Scheduled day/night mode
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
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
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’’
Infrared Sensors Advantage Disadvantage • Less expensive than the ultrasonic sensor • Range was not as wide as the ultrasonic sensor Infrared
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’’
System Block Diagram Camera Captures an Image Image processing Data analysis Server Generates statistics Outputs data Display
Image capture • The network camera captures an jpg image from the mjpg stream
Image processing • The captured image is then processed through the code to accurately object detect cars in the parking lot
Data output • The program then outputs to a .csv file with information based on the processed image.
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.
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
System refresh & update display • The website is then updated with the proper graphical view of the vacant parking lots.
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.
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
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
Data Preparation • Collect positive images. • Obtain negative sample set. • Create info files. • Generate vec files. • Train cascade.
Object Marking • For the positive sample set, object marking was required. The positive info file looked like the following:
Software • C++ • OpenCV Library • Single Run, repeated using a batch file • Can be continual • Low CPU usage • Scalable
Software Flow • Capture image • Gray scale • Object detection • Region of interest rectangle comparing • Output to .csv file • Sleep for set time interval repeat
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
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
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
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.
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.
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