A Vision Based Anthropometric

Scanner

Project Management and Software Development

Final Presentation

Ahmed El-Gazzar

Supervisor : Benjamin Busam

Problem Statement

July 13, 2016A vision based anthropometric Scanner Slide 2

Buying a bicycle

It’s not that simple!

http://www.wikihow.com/Buy-a-Bicycle

Problem Statement

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An optimum bicycle provides:

Comfort

Safety

Injury prevention

Peak performance

http://www.twowheelsgood.co.uk/fit-services.php

Our Approach

July 13, 2016A vision based anthropometric Scanner Slide 4

Fitting a bike to the costumer based on pose estimation

Overview

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Procedure breakdown

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• Implemented in Matlab using MatConvNet.

• Convolutional Neural Network.

• Trained using the Leeds Sports Pose Dataset.

Network Training

Camera Calibration

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Tens of images with checkerboard and Bicycle

Intrinsic Camera Parameters calculated and stored

Pose Estimation

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Pose estimation

Human region is cropped

CNN validation

14*2 outputs extracted and drawn

Image to World

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Real pose estimation

Bicycle images collected

Tires detected.

Tires radiuses and centers distance calculated

Bicycle parameters

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Human body parameters extracted

Bicycle parameters calculated

Formulas

Tables

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SHRH

Bicycle parameters

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𝑆𝐻 = 𝑅 ∗ 0.885 𝑂𝑅 = 𝑅 + 𝐴 ∗ 𝐻 − 𝐴

Roadbike.de magazine, 10, 2012

Dynamic fitting

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Pose on the bicycle

Video Capturing

Results were not satisfactory

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17-Apr-2016 27-Apr-2016 7-May-2016 17-May-2016 27-May-2016 6-Jun-2016 16-Jun-2016 26-Jun-2016 6-Jul-2016 16-Jul-2016

Reading Papers

Understanding Previous Codes

Camera Calibration

Training & Testing the oldalgorithm

Videos Data Collection

Training & Testing the newalgorithm

Updating the GUI

Visualisation

Dynamic fitting

Final Report

Gantt Chart

Created with Microsoft Excel

Project Outlook

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Create a customized dataset

Retrain network

Optical flow

Lessons Learned

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Deep Learning in action

Connecting different parts

Time management

Work presentation

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Thank you for your attention!