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Camshift -based Real-time Multiple Vehicles Tracking for Visual Traffic Surveillance

Camshift -based Real-time Multiple Vehicles Tracking for Visual Traffic Surveillance. 報告人 : 林福城 指導老師 : 陳定宏. From Res. Center of Intell. Transp. Syst., Beijing Univ. of Technol., Beijing, China By Zhe Liu ; Yangzhou Chen ; Zhenlong Li

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Camshift -based Real-time Multiple Vehicles Tracking for Visual Traffic Surveillance

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  1. Camshift-based Real-time Multiple Vehicles Tracking for Visual TrafficSurveillance 報告人:林福城 指導老師:陳定宏 From Res. Center of Intell. Transp. Syst., Beijing Univ. of Technol., Beijing, China By Zhe Liu ; Yangzhou Chen ; Zhenlong Li Appears in: Computer Science and Information Engineering, 2009 WRI World Congress on

  2. Outline 1.Introduction 2.Moving object detection 2-1.Conscutive image difference 2-2.Backgrout difference 3.Moving object tracking 3-1.Review of Tracking Algorithm 3-2.Camshift Multiple Vehicle Tracking 4.Traffic Parameters Estimation 5.Experimental Results 6.Conclusions

  3. 1.Introduction • Traffic management and information systems: • 1.Inductive loop detectors • 2.Visual surveillance systems • Our approach specifies three sub process: • 1. Vehicle Extraction : Consecutive image difference • Background difference • 2. Vehicle Tracking • 3. Traffic Parameter Estimation

  4. 2.1 Consecutive Image Difference D(x,y) is the difference image. Mask(x,y) is the image after binarization.

  5. 2.2 Background Difference(1) It assume a moving object would not stay at the same position for more than half of n frames.

  6. 2.2 Background Difference(2)

  7. 2.Moving Object Detection Conclusion

  8. 3-1.Review of Tracking Algorithm • 1.Tracking based on a moving object region: Size, Color, Shape, Velocity, Centroid • 2.Tracking based on an active contour of a moving object • 3.Tracking based on a moving object model • 4.Tracking based on selected features of moving objects : Corner

  9. 3-2.CamShift Multiple Vehicle Tracking

  10. Search in the model’s neighborhood in next frame Find best candidate by maximizing a similarity func. Model Candidate … … Current frame Mean-Shift Object TrackingGeneral Framework: Target Localization Start from the position of the model in the current frame Repeat the same process in the next pair of frames

  11. Choose a reference target model Choose a feature space Represent the model by its PDF in the feature space Quantized Color Space Mean-Shift Object TrackingTarget Representation Kernel Based Object Tracking, by Comaniniu, Ramesh, Meer

  12. SimilarityFunction: Mean-Shift Object TrackingPDF Representation Target Model (centered at 0) Target Candidate (centered at y)

  13. 4.Traffic Parameters Estimation • 1.Vehicle count • 2.Vehicle average speed • 3.Vehicle size

  14. Experimental Results

  15. Conclusions • In this paper, we have presented methods for detecting and tracking multiple vehicles in an outdoor environment. Each detected vehicle is assigned a camshift tracker which can effectively track object with different size and shape under different illumination conditions. • The method fails to handle long slow moving vehicle queue.

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