1. Establishing an image-based ground truth for validation of sensor data-based room occupancy detection
Steffen Petersen, Theis Heidmann Pedersen, Kasper Ubbe Nielsen, Michael Dahl Knudsen
Published in 2016 Energy and Buildings
Presented by Xiaoyu (Veronica) Liang

2. Backgrounds
Buildings consume almost 40% of the energy used in most countries around the world.
Heating, Ventilation, and Air Conditions (HVAC) systems are responsible for almost half of the energy consumed in buildings.
Occupant based HVAC control approach can significantly improve energy savings.
EuroStat 2010
Consumption by End Use
EIA’s 2009 Residential Energy Consumption

3. Motivations
Data-based occupancy detection methods need to be evaluated
Automate the recording of the ground truth

4. Detecting persons in the cameral field of view
Proposed Method
Detection
Detecting persons in the cameral field of view
Tracking
Determines whether detected persons enter or leave the room

5. Detection
Remove zero-value pixels

6. Detection

7. Tracking Kalman filter and munkres assignment algorithm
walls
Camera field of view
Kalman filter and munkres assignment algorithm
A simple line crossing logic.
Any detected moving object has to cross the Primary Virtual Lines and a Secondary Virtual Lines to be counted as entering or leaving the room.
Robust against any small movement of objects in the image background.
Primary virtual lines
Secondary virtual lines
walls

8. Performance Tests and Results
64-bit PC
Intel I7-4710HQ CPU
16 GB RAM
30 frames per second
Stress Test
Determine the appropriate parameters.
Function Test
Test the ability of the proposed methods.
Room Occupancy Test
Test automatic real-time counting of occupants in normal office or dwelling rooms

9. Performance Tests and Results – Stress Test
Many persons crossing the camera field of vie within a short time period.
Mount the camera at the top of main internal stair in a building with a high flow of persons.
Results are compared with two independent and parallel series of manual people counting.
Repeat multiple times to determine the best values.
Results showed:
75 out of 76, entering
51 out of 52, leaving
No False Positive & Not False Negative
Accuracy 98.4%

10. Performance Tests and Results – Function Test
Detect and track one and multiple persons moving in and out of the camera field of view in different ways.

11. Performance Tests and Results – Room Occupancy Test
A 3-week test period.
An office room occupied by 3 persons.
Detect 296 entries and 297 exits.
Compare with the parallel supervised observations (299 entries and exits) :
99.2% accuracy
No False Positive
No False Negative
A 7-hours test period.
An office room occupied by multiple persons.
Detect 53 entries and 53 exits.
Compare with the parallel supervised observations:
100% accuracy
No False Positive
No False Negative

12. Questions?
Questions?