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Mobile Object Detection through Client-Server based Vote Transfer

Mobile Object Detection through Client-Server based Vote Transfer. CVPR 2012 poster. Outline. Introduction Frame detection Mobile application blue-print Experiment Conclusion. Introduction. Android OS. Introduction. Short video sequence. Introduction. Main Contribution:

jolie-gibson
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Mobile Object Detection through Client-Server based Vote Transfer

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  1. Mobile Object Detection through Client-Server based Vote Transfer CVPR 2012 poster

  2. Outline Introduction Frame detection Mobile application blue-print Experiment Conclusion

  3. Introduction Android OS

  4. Introduction Short video sequence

  5. Introduction • Main Contribution: • Novel hough forest based multi-frame object detection framework • Vote transfer • Client-server framework

  6. Frame detection • Single-Frame detection • Hough forest [10] [10] J. Gall and V. Lempitsky. Class-specific hough forests for object detection. In CVPR, 2009.

  7. Frame detection P={L,c,d}

  8. Frame detection • Multi-Frame detection • Motivation • Different express with single frame detection

  9. Frame detection • Multi-Frame detection • Vote transfer

  10. Frame detection • Multi-Frame detection • Vote transfer

  11. Frame detection

  12. Mobile application blue-print Client-server

  13. Experiment • Datasets • A new multi-view dataset that we collected • the Car Show Dataset introduced by Ozuysalet al [19] • http://www.eecs.umich .edu/vision/Mvproject.html [19] Pose estimation for category specific multiview object localization. In CVPR, 2009

  14. Experiment • Vote transfer • Giving each a weight • Reference frame’s weight=1 • Other frames’s weight= 2 -i/10 , i={10,20,30,40,50}

  15. Experiment Single vs Multi-frame Performance

  16. Experiment Single vs Multi-frame Performance

  17. Experiment Tracking analysis

  18. Experiment Image resolution

  19. Experiment • Mobile platform: Client-Server analysis • Client: • Motorola Atrix4g dual-core phone Android 2.2 • Image size:640*480 • Server: • 2.4GHZ triple-core desktop For more information to Motorola Atrix http://www.motorola.com/us/consumers/Motorola-ATRIX-4G/72112,en_US,pd.html?cgid=mobile-phones

  20. Experiment • Mobile platform: Client-Server analysis • Single frame • Multi frame

  21. Conclusion A new approach to multi-frame object detection using Hough Forest Realistic implementation Client-server approach on mobile platform About future work: Pose estimation, how view-point changes can foster pose estimation

  22. Thanks for your listening.

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