1. Background Subtraction for Urban Traffic Monitoring using Webcams Master Graduation Project Progress Presentation Supervisor: Rein van den Boomgaard Mark Smids September 12 th 2006

2. Overview Introduction System Overview Background Subtraction Methods Progress Conclusions The Next Presentation Questions

3. Introduction System Overview Background Subtraction Methods Progress Conclusions The Next Presentation Questions Goals of this research Literature study on traffic monitoring Literature study on background subtraction methods and shadow detectors Implement a suitable background subtraction method and shadow detector for this application

4. Introduction System Overview Background Subtraction Methods Progress Conclusions The Next Presentation Questions A number of camera’s producing streams Camera calibration and initialization For each stream: Background subtraction Shadow detection and elimination Object tracking Update traffic parameters Video summarization Control panel (GUI) System Overview

5. Introduction System Overview Background Subtraction Methods Progress Conclusions The Next Presentation Questions A number of camera’s producing streams Camera calibration and initialization For each stream: Background subtraction Shadow detection and elimination Object tracking Update traffic parameters Video summarization Control panel (GUI) System Overview

6. Introduction System Overview Background Subtraction Methods Progress Conclusions The Next Presentation Questions System Overview Background Subtraction Component consists of: Pre-processing stage Background modeling Foreground detection Data validation Incorporated: Shadow Detection

7. Background Subtraction Methods Introduction System Overview Background Subtraction Methods Progress Conclusions The Next Presentation Questions #1 Deterministic Approach Create an initial background model from the first N frames Use mean? Use median? For each new frame, subtract it from the background model to obtain a binary mask Update the background model

8. Background Subtraction Methods Introduction System Overview Background Subtraction Methods Progress Conclusions The Next Presentation Questions #2 Statistical Approach Model each pixel in the history frames by a mixture of Gaussians Why a mixture? Build a background model by selecting those Gaussians that correspond to background pixels Update the background model by updating weight, mean and covariance parameters

9. Background Subtraction Methods Introduction System Overview Background Subtraction Methods Progress Conclusions The Next Presentation Questions How determine those components that model the background? Observation: these Gaussians have the most supporting evidence and lowest variances Order the K distributions in the mixture by the value of The first B distributions are chosen as the background model, where:

10. Progress Introduction System Overview Background Subtraction Methods Progress Conclusions The Next Presentation Questions What’s done? Literature study about traffic monitoring, background subtraction methods and shadow detection Implementation of two background subtractors Implementation of two a shadow detectors Written the parts of my thesis covering the above

11. Progress Introduction System Overview Background Subtraction Methods Progress Conclusions The Next Presentation Questions What still to do? Testing the two background subtraction systems with prerecorded videos Finishing my thesis/paper

12. Conclusions Introduction System Overview Background Subtraction Methods Progress Conclusions The Next Presentation Questions Urban traffic monitoring is more challenging than traffic monitoring on highways The background subtraction component is the most important one in the chain Real time processing possible using the OpenCV library.

13. The Next Presentation Introduction System Overview Background Subtraction Methods Progress Conclusions The Next Presentation Questions Discuss the shadow detectors Discuss the testing process Compare both background subtraction methods and shadow detectors Concluding which method is best given the urban traffic setting

14. Questions… Introduction System Overview Background Subtraction Methods Progress Conclusions The Next Presentation Questions

15. You should have asked about this… Introduction System Overview Background Subtraction Methods Progress Conclusions The Next Presentation Questions Hidden Section Update Equations: Z. Zivkovic, “Improved Adaptive Gaussian Mixture Model for Background Subtraction” MoG :