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Background Subtraction for Urban Traffic Monitoring using Webcams

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

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Background Subtraction for Urban Traffic Monitoring using Webcams

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

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

  3. Introduction • 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. System Overview • 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)

  5. System Overview • 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)

  6. System Overview • Introduction • System Overview • Background Subtraction Methods • Progress • Conclusions • The Next Presentation • Questions • 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: • MoG: Z. Zivkovic, “Improved Adaptive Gaussian Mixture Model for Background Subtraction”

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