1. CS Lab Usage
Logan Balow

2. Requirements The Camera activates based on motion
Capture snippets of video
System recognizes people compared to motion
Collect usage times and numbers
Develop Admin side that reports statistics and maintains video
Implement a camera based system that monitors the CS Lab and usage
Using the Foscam-fi8910w IP Camera
WPF C# application in Visual Studios 2017
Detect motion from previous frames
Capture video based on motion or user
Person detection algorithm
Collect and report the data

3. Image Data Flow Wireless Router Camera is wireless (minus the power)
Allows you to connect remotely using its IP web address
School firewall prevents p2P access so both the computer and camera have to be on the same network to access currently
Program connects to the camera using a username and password, then begins querying and collecting the frames from the camera
Rate at about 30 per second at 640x480 res

4. Haar Cascade Structures
Method proposed by Paul Viola and Michael Jones
Machine learning approach using positive and negative images
Utilizes integral image
Reduces any sized image to an operation involving just four pixels at a time
Starts with Eyes, then nose/bridge of nose area etc.
Must pass all “Stages” to be accepted as a face
- effective object detection method proposed by Paul Viola and Michael Jones 
Initially, the algorithm needs a lot of positive images (images of faces) and negative images (images without faces) to train the classifier.
Each feature is a single value obtained by subtracting sum of pixels under the white rectangle from sum of pixels under the black rectangle.
Not 24x24 = features
To solve this, they introduced the integral image. However large your image, it reduces the calculations for a given pixel to an operation involving just four pixels
 most of them are irrelevant 
Pass through each stage and throws out the image if a portion doesn’t meet the stage. Must pass all stages in order to be accepted as a face

5. Facial Detection BitmapSouce Bitmap EMGU Image
BitmapSource to Bitmap to EMGU Image, then greyscale, to face detection (rectangles)
EMGU Image

6. Video Recording List<Images> EMGU Video Writer User
Can Record based on User interaction or motion recording
Motion tracking must be on first
User
Stops Recording
EMGU Video Writer

7. Spreadsheet Generation
Select if you would like a report by hour or by day
C# has using Microsoft.Office.Interop; library
Allows you to dynamically generate and save excel files

8. Difficulties
Using EMGU_CV means no access to altering the algorithm for facial detection
People must be perpendicular to the camera
No depth perception
Counting people only once
Location
Bottleneck of the lab entrance
Converting the frame from the camera to EMGU_CV Image class
CPU Usage
EMGU DOCUMENTATION
Lack of it for newer items
Detection is not a perfect science
No way to tune the facial detection
Limited to how it works.
Must Be perpendicular to camera
Flat 2D image no depth
Location of the lab allows for stacking up of people and may not get everyone
Converting the frame from BitmapSource to EMGU_CV library
CPU Usage initially 80%-90% idle
Brought it down to 10% with motion detection on with spikes to 50%

9. DEMO TIME!

10. Strategies
- Pretty waterfall type approach initially pretty even flow

11. Plan was straightforward kind of a waterfall approach where you just work down the line and test as you go
Developed into this mess that you see
Lots of testing and going back to the drawing board
Past 3 weeks have been testing recording human data
Count people once and only once
Dr Pankratz Pointed me to EGMU
Also gave me Kinect book which helped

12. Classes That Helped
CSCI 110: Eased me into coding got me more interested
CSCI 205: Opened me to OOP
CSCI 220: Advanced knowledge of Files and data structures
CSCI 350: Event programming knowledge
110: cause intro
205: Elementary Data structures
220: Advanced File and Data
350: Event Programming

13. Where to Go From Here Firewall Problems
Allow for remote viewing and access to data
Smart Home extensions
Lights, Doors, Temperature, Pet interaction
Machine learning project for personal person detection
Gestures with person detection
Kinect Camera or other
Firewall
Currently no P2P access within the snc network unless IT opens certain ports for a short time
Perhaps find a way around this
Will allow for remote viewing and accessing the data from anywhere
Further the data collected and improve administrative side
Smar Home extensions
Smart home items such as lights, doors, temperatures
Pet interractions, such as viewing, talking to, feeding remotely
Machine learning project
Develop a similar EMGU_CV process for detecting a person from an image or depth image
Explore this project with a different camera and different extensions
Kinect and gestures

14. Questions?
Questions? Comments? Stand-up comedy?