Online Camer Calibration

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Summer research project that include evaluate two online camera calibration algorithms and use the algorithm with better test result to perform back-projection and geo-location for pedestrian to be virtualized in 3D model.

Technology

Online Camera Calibration
Fei-Fei Zheng
Advisor: James Elder

Background (ISSUM)
Challenges
◉ Population mobility
◉ Transit and roadways
◉ Greenhouse emissions
◉ Accessibility for people with disabilities
Intelligent System for Sustainable Urban Mobility
Computer Vision
◉ Modeling
◉ Simulation
◉ Visual analysis

Background (ISSUM)
Research Themes
◉ 3D cityscape modeling
◉ Visual analysis of crowds
◉ Intelligent transportation systems
◉ Mobile augmented reality
◉ Integrated systems

Pedestrian Analysis
Video Feed
Detection, Tracking,
Pose Estimation 3D Scene
Online Camera
Calibration
Models
Virtual agents in 3D model
◉ Camera parameters
◉ OpenPose algorithm
◉ 3D models

Camera Calibration
◉ Intrinsic parameters
◉ Extrinsic parameters
◉ Distortion coefficients
Radial Distortion No Distortion
Calibrated
Original

Offline Camera Calibration
Ground
Tilt Angle
◉ Intrinsic parameters (focal length, etc)
◉ Extrinsic parameters (3D rotation, etc)
Pan-Tilt-Zoom (PTZ) Camera
Checkerboard

1. Manhattan Frame Estimation
• 3D Rotation Estimation
(Tal & Elder, ACCV 2012)
 Edge detection
 Probabilistic Hough transform
 Focal Length Estimation
Online Calibration

2. Estimate Camera Tilt from Motion without Tracking
(Elassal & Elder, CRV 2017)
 Optic flow
 Rectified speed
RectifiedOriginal
Online Calibration
a) Marathon dataset b) Optical flow field c) Scatter plot of speed

Online Calibration
60
65
70
75
80
Tilt (Cam1) Tilt (Cam2)
TILTANGLE(DEGREE)
Ground Truth Manhattan Motion
Tilt Angle Estimation
4.2
4.4
4.6
4.8
5
5.2
5.4
FL (Cam1) FL (Cam2)FOCALLENGTH(MM)
Ground Truth Manhattan
Focal Length Estimation

From 2D to 3D
Courtesy: Dr. Pio Claudio
Back-projection of L, R Ankles
OpenPose

Future Work
◉ Crowd counting
◉ Unusual activity detection
◉ Evacuation route planning
◉ Optimization of retail layouts

Credits
Special thanks to all the people who helped and worked with me
for this project:
◉ Supervisor: Professor James Elder
◉ Mentor: Dr. Pio Claudio
◉ Yiming Qian, Attila Gall, David Racchia, William Xiang, Sun Park

14
Any questions ?
You can find me at
◉ ffzheng@my.yorku.ca
Thanks!

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Online Camer Calibration

1. Online Camera Calibration Fei-Fei Zheng Advisor: James Elder

2. Background (ISSUM) Challenges ◉ Population mobility ◉ Transit and roadways ◉ Greenhouse emissions ◉ Accessibility for people with disabilities Intelligent System for Sustainable Urban Mobility Computer Vision ◉ Modeling ◉ Simulation ◉ Visual analysis

3. Background (ISSUM) Research Themes ◉ 3D cityscape modeling ◉ Visual analysis of crowds ◉ Intelligent transportation systems ◉ Mobile augmented reality ◉ Integrated systems

4. Pedestrian Analysis Video Feed Detection, Tracking, Pose Estimation 3D Scene Online Camera Calibration Models Virtual agents in 3D model ◉ Camera parameters ◉ OpenPose algorithm ◉ 3D models

5. Camera Calibration ◉ Intrinsic parameters ◉ Extrinsic parameters ◉ Distortion coefficients Radial Distortion No Distortion Calibrated Original

6. Offline Camera Calibration Ground Tilt Angle ◉ Intrinsic parameters (focal length, etc) ◉ Extrinsic parameters (3D rotation, etc) Pan-Tilt-Zoom (PTZ) Camera Checkerboard

7. 1. Manhattan Frame Estimation • 3D Rotation Estimation (Tal & Elder, ACCV 2012)  Edge detection  Probabilistic Hough transform  Focal Length Estimation Online Calibration

8. 2. Estimate Camera Tilt from Motion without Tracking (Elassal & Elder, CRV 2017)  Optic flow  Rectified speed RectifiedOriginal Online Calibration a) Marathon dataset b) Optical flow field c) Scatter plot of speed

9. Online Calibration 60 65 70 75 80 Tilt (Cam1) Tilt (Cam2) TILTANGLE(DEGREE) Ground Truth Manhattan Motion Tilt Angle Estimation 4.2 4.4 4.6 4.8 5 5.2 5.4 FL (Cam1) FL (Cam2)FOCALLENGTH(MM) Ground Truth Manhattan Focal Length Estimation

10. From 2D to 3D Courtesy: Dr. Pio Claudio Back-projection of L, R Ankles OpenPose

11. Courtesy: Dr. Pio Claudio, Sun Park

12. Future Work ◉ Crowd counting ◉ Unusual activity detection ◉ Evacuation route planning ◉ Optimization of retail layouts

13. Credits Special thanks to all the people who helped and worked with me for this project: ◉ Supervisor: Professor James Elder ◉ Mentor: Dr. Pio Claudio ◉ Yiming Qian, Attila Gall, David Racchia, William Xiang, Sun Park

14. 14 Any questions ? You can find me at ◉ ffzheng@my.yorku.ca Thanks!

Online Camer Calibration

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