CVPR 2012 POSTER

Mobile Object Detection through Client-Server based

Vote Transfer

Outline

IntroductionFrame detectionMobile application blue-printExperimentConclusion

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 forobject 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] http://www.eecs.umich .edu/vision/Mvproject.html

[19] Pose estimation for categoryspecific 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 analysisClient:

Motorola Atrix 4g dual-core phone Android 2.2

Image size:640*480Server:

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

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

Thanks for your listening.