Mobile object detection through client server based vote transfer
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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:

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Mobile Object Detection through Client-Server based Vote Transfer

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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

Outline

Introduction

Frame detection

Mobile application blue-print

Experiment

Conclusion


Introduction

Introduction

Android OS


Introduction1

Introduction

Short video sequence


Introduction2

Introduction

  • Main Contribution:

    • Novel hough forest based multi-frame object detection framework

    • Vote transfer

    • Client-server framework


Frame detection

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 detection1

Frame detection

P={L,c,d}


Frame detection2

Frame detection

  • Multi-Frame detection

    • Motivation

    • Different express with single frame detection


Frame detection3

Frame detection

  • Multi-Frame detection

    • Vote transfer


Frame detection4

Frame detection

  • Multi-Frame detection

    • Vote transfer


Frame detection5

Frame detection


Mobile application blue print

Mobile application blue-print

Client-server


Experiment

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


Experiment1

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}


Experiment2

Experiment

Single vs Multi-frame Performance


Experiment3

Experiment

Single vs Multi-frame Performance


Experiment4

Experiment

Tracking analysis


Experiment5

Experiment

Image resolution


Experiment6

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


Experiment7

Experiment

  • Mobile platform: Client-Server analysis

    • Single frame

    • Multi frame


Conclusion

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


Mobile object detection through client server based vote transfer

Thanks for your listening.


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