1. Detecting Room Occupancy with Pi Camera
Final Report
Muneeb Alvi

2. Original Goal
Python
OpenCV
Open Source Computer Vision Library

3. What I completed
Went from using image differencing to being able to detect occupants individually in images
Able to process images in real time
Able to take previously taken images and identify occupants

4. Methods used OpenCV library HOG – Histogram of Oriented Gradients
Popular Open Source Computer Vision Library
Contains HOG and Linear SVM methods
HOG – Histogram of Oriented Gradients
Used for object detection in computer vision (detecting humans)
Linear SVM methods
Machine learning linear support vector machine methods
Used for classification (person vs another object)

5. Components Hardware Send unprocessed image Process image and identify
people
Captures images which are
processed by raspberry pi cpu

6. Components Software
Python for interacting with camera and processing image
Java for SIS server (simulate sending s/alerts)
Interact through file reading/writing
Python writes occupancy status to file
Java reads occupancy status and sends message to alert component
Producer/Consumer model

7. Components Software Python Image processing program Alert Component
Java SIS Server
Alert Component
Send occupancy status (occupied/empty)
and number of occupants
after processing image
Forward message from
Python program to
any registered components
Message
Display corresponding Alert on Monitor
Action

8. Remaining Challenges Current implementation is not 100% accurate
Cannot detect all occupants
Solution: Use better algorithm or a better camera
Raspberry pi has computing limitations
Takes longer than expected to process images
Solution: Use better/more expensive hardware or optimize algorithm

9. Demo