1 / 15

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

denton-mercado
Télécharger la présentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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.

More Related