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

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 Ozuysal et 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 Atrix 4g 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.