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

Outline Introduction Frame detection Mobile application blue-print Experiment Conclusion

Introduction Android OS

Introduction Short video sequence

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

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

Frame detection P={L,c,d}

Frame detection Multi-Frame detection  Motivation  Different express with single frame detection

Frame detection Multi-Frame detection  Vote transfer

Frame detection Multi-Frame detection  Vote transfer

Frame detection

Mobile application blue-print Client-server

Experiment Datasets  A new multi-view dataset that we collected  the Car Show Dataset introduced by Ozuysal et al [19]  [19] Pose estimation for category specific multiview object localization. In CVPR, 2009

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}

Experiment Single vs Multi-frame Performance

Experiment Single vs Multi-frame Performance

Experiment Tracking analysis

Experiment Image resolution

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 ATRIX-4G/72112,en_US,pd.html?cgid=mobile-phones

Experiment Mobile platform: Client-Server analysis  Single frame  Multi frame

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

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