A Real Time Virtual Dressing Room Application using Opencv

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ISSN 2395-1621

A Real Time Virtual Dressing Room

Application using Opencv

#1

Ms. Rshami S. Shinkar, ^#2Prof. Nagaraju Bogiri

1[email protected]

2[email protected]

#12Department of Computer Engineering K. J. College of Engineering Pune.

ABSTRACT ARTICLE INFO

The dresses worn by virtual character is done with a real time processing of cloth by a data drive approach, is proposed. It starts with creation of clone of a person prior to real-time simulation, by analyzing height and skin colour tone parameter to give exact look to skeleton. The system will be entirely equipped with sensors like motion sensor, light sensor, camera which is also hardware of our system which control by Graphical User Interface Software. Hardware uses latest depth camera of Kinect Sensor along with Unity SDK. The controllers will be managed by an operating system which will communicate with the user interactive software. The system will provide smart interface to retailer and end user customers. This smart system can increase the level of marketing than the current scenario. This system will realize smart solution for dressing and efficiently solve the issue related to retailers and end user customer.

Number of interactive and effective operation supported by our system are as follows:- 1) 3D mannequin is adjusted automatically according to the shopper’s skeleton measurement. 2) Virtual trial of variety of cloths after selecting them. 3) Real time simulation of movement of cloths according to mannequin with online resizing/fitting of cloths on it. It is our ultimate goal to develop and integrate different key technologies into interactive virtual clothing store, distributed, where user can select cloths and try on clone that are adjusted to their body measurements. We can overcome the problem of return items and risk in buying process.

Keywords: Depth Image, Kinect Sensor, Augmented Reality, Unity SDK, Skeleton

Article History

Received: 24^thDecember 2018 Received in revised form : 24^thDecember 2018

Accepted: 26^th December 2018 Published online :

31^st December 2018

I. INTRODUCTION

Trying clothes in clothing stores is usually a time consuming activity. Besides, it might not even be possible to try-on clothes in such cases as online shopping. Our motivation here is to increase the time efficiency and improve the accessibility of clothes try on by creating a virtual dressing room environment.

The problem is simply the alignment of the user and the cloth models with accurate position, scale, rotation and ordering. First of all, detection of the user and the body parts is one of the main steps of the problem. In literature, several approaches are proposed for body part detection, skeletal tracking and posture estimation.

The usage of web camera makes it more easy on the cost for the users of online shopping.

The implementation by OpenCV makes it more platform independent and portable and there by accessible in any form of device.

Our approach can be summarized as follows:

 Extraction of the user from the video stream by using depth and user label data.

 Positioning of the 2D cloth models by using the skeletal tracker.

 Scaling of the models by using the Euclidean distance between the body joints and distance of the user from the sensor.

 Skin color detection in order to prevent unwanted occlusions of body parts and the model,

 Superimposition of the model on the user.

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to test practically the performance. The user interface allows the user to choose a dress by means of hand movements.

Fig 1. Flow diagram

Extraction of user allows creating an augmented reality environment by isolating the user area from the video stream and superimposing it onto a virtual environment in the user interface. Furthermore, it is useful to determine the region of interest that is also used for skin detection. The Kinect SDK provides the depth image and the user ID. When the device is working, depth image is segmented in order to separate background from the user. The background is removed by blend in the RGBA image with the segmented depth image for each pixel by setting the alpha channel to zero if the pixel does not lie on the user.

II. REVIEW OF LITERATURE

The current method of online shopping does not guarantee the perfect size of the clothing. This results in a number of products being returned and the time taking to replace it with the correct sized one is long.

This is a major setback for the online shopping industry.

The various approaches to obtain the desired results are as follows,

Srinivasan K. and Vivek S. [1], Growth in online shopping and the wish of people to have to enjoy its maximum utilization on purchase of dress with complete satisfaction of personal realization justifies the need to develop an algorithm which virtually dresses people with the selected dress. human silhouette with variable background and noisier environment. Which is the more challenging task in still image using image processing.

Ari Kusumaningsih and EkoMulyantoYuniarno [2],A virtual dressing room for Madura batik dress has been successfully developed. The proposed system has a purpose tom make dressing room specialized for Madura batik clothes supposed to create attention from customer and should contributes in improving sales performance and promote Madura’s heritages as also.

Efficient and fast computation methods needed to process numerous 3D models. So that, we don’t have to use high performing computer for implementing this virtual dressing room.

Ting Liu and LingZhi Li [3], work uses user extraction from Kinect video stream and avatar system for skeletal tracking to align the clothes’ models with users. And a virtual dressing software prototype is developed allowing clothes’ 3D models to overlay users and were convenient to view in front, side and back perspectives. Furthermore, improving clothes modeling approaches that achieve rapid reconstruction based on real clothes is also of great use.

Stephen Karungaru and Kenji Terada [4], in this Project, they propose a method to acquire human body length / perimeter easily using Kinect. Experimental results confirmed that human data can be acquired from Kinect sensor. We also confirmed problems in case of error in acquired data. Future issues include improving the accuracy of acquisition of person's data and the CG.

Dr. Anthony L. Brooks and Dr. EvaPetersson Brooks [5], the open-structured surveys received wide-ranging input from the public attending the live demonstrations at Malls and Messe events. 13 wheelchair-bound individuals gave direct input as well as others who were either friends or associated with a wheelchair- bound person that they considered would benefit from a dedicated adaptation of the product. Yet that distance had to be close enough to allow the person an operable view of the interface control detail.

Reizo NAKAMURAand Masaki IZUTSU [6], this paper show processes that estimate of body suites size.

First, person recognition be got by Kinect. And, person area in the image be extracted using person recognition data. Next, user’s mark points are extracted using contour tracing. The size of the body suites was presumed using it.

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TTILE AUTHOR YEAR Method Proposed Disadvantages Implementat

ion Of

Virtual Fitting Room Using Image Processing

Srinivasan K. and Vivek S.

2017 Growth in online shopping and the wish of people to have to enjoy its maximum utilization on purchase of dress with complete satisfaction of personal realization justifies the need to develop an algorithm which virtually dresses people with the selected dress. human silhouette with variable background and noisier environment. This is the more challenging task in still image using image processing.

Virtual Fitting Room disadvantages is ressing the quantity purchased is comparatively less. This is because of the fact that people wish to know how cloths looks on oneself and how both the top and bottom

matches together and also how the size of clothes fits the contour of oneself.

User Experience Measureme

nt On

Virtual Dressing Room Of Madura Batik Clothes

Ari Kusumanin gsih and EkoMulyan toYuniarno

2017 A virtual dressing room for Madura batik dress has been successfully developed. The proposed system has a purpose tom make dressing room specialized for Madura batik clothes supposed to create attention from customer and should contributes in improving sales performance and promote Madura’s heritages as also.

Efficient and fast computation methods needed to process numerous 3D models. So that, we don’t have to use high performing computer for implementing this virtual dressing room.

The drawback is that the dress usually displays in 2 dimensions, so it looks like the dress is attached only to the front of the body. Our system used 3D virtual dresses which wrapping around the consumer body.

Real-time 3D Virtual Dressing Based on Users

Ting Liu and

LingZhi Li

2017 This work uses user extraction from Kinect video stream and avatar system for skeletal tracking to align the clothes’ models with users. And a virtual dressing software prototype is developed allowing clothes’ 3D models to overlay users and were convenient to view in front, side and back perspectives. Furthermore, improving a clothes modeling approaches that achieves rapid reconstruction based on real clothes is also of great use.

The person in front of the

Microsoft Kinect

is interacting at a certain distance and inside a limited area. The green line indicates the appropriate height placement of the Microsoft Kinect.

Body Physical Measureme nt using Kinect for Vitual Dressing Room

Stephen Karungaru and Kenji Terada

2017 In this Project, we propose a method to acquire human body length / perimeter easily using Kinect. Experimental results confirmed that human data can be acquired from Kinect sensor. We also confirmed problems in case of error in acquired data. Future issues include improving the accuracy of acquisition of person's data and the CG.

One disadvantage of net shopping is that there are times when size errors occur. This is because the product cannot be actually picked up and tried on.

Towards an Inclusive Virtual

Dr.

Anthony L.

Brooks and

2014 The open-structured surveys received wide-ranging input from the public attending the live demonstrations at

Kinect Sensor place on fix position and covered limited area but wheelchair not

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Room for Wheelchair- Bound Customers

Dr.

EvaPeterss on Brooks

Malls and Messe events. 13 wheelchair-bound individuals gave direct input as well as others who were either friends or associated with a wheelchair-bound person that they considered would benefit from a dedicated adaptation of the product.

Yet that distance had to be close enough to allow the person an operable view of the interface control detail.

adjustable this particular area then result not getting accurate.

Estimation Method of Clothes Size for Virtual Fitting Room with Kinect Sensor

ReizoNAK AMURAan d Masaki IZUTSU

2013 This paper show process that estimate of body suites size. First, person recognition be got by Kinect. And, person area in the image be extracted using person recognition data. Next, user’s mark points are extracted using contour tracing. The size of the body suites was presumed using it.

The proposed system are used to two Kinect sensors but this two sensor many times different output.

Realistic Simulation in Virtual Fitting Room Using Physical Properties of Fabrics

PoonpongB oonbrahma and CharleeKae wrat

2015 Using the physical parameter from our experiment, the appearance of the fabrics under simulation can be predicted. The simulation results can tell the difference among customers wearing jean, satin, silk or cotton, which will be very useful for setting up the virtual fitting room.

This system are used only 2D, user only view front side images.

Real-time virtual fitting with body

measuremen t and motion smoothing

UmutGülte pe and UğurGüdü kbay

2014 We propose a novel virtual fitting room using depth sensor data. The framework yields a realistic fitting experience for standard body types with customized motion filters, body measurement and physical simulation.

The proposed scaling method adjusts the avatar's body size parameters and determines a suitable apparel size, and prepares the collision mesh and the physics simulation. In future work, we would like to improve the quality of the measurements and visual scaling by using data from an RGB sensor as well, because it provides additional data. We would like to increase the number of collision spheres for better collision detection

The most important limitation of the framework is it sinsufficiency In customization.

III. CONCLUSION

Here the virtual dressing room application requires only a front image. For each product to superimpose it onto the user and the 2D graphics of the product seem to be relatively satisfactory and practical for many uses. The presented methodology this used to align the models with the user and to test the procedure under different conditions.

This system covered all drawback and experiments have resulted with acceptable performance rates for regular postures. There are many possible implementations regarding the model used for fitting. It is possible to apply a

homographic transformation to the images rather than the simple scale-rotate technique in order to matchmultiple joints altogether although it would require more computation. Another alternative could be using many pictures at different angles so that it would be possible to create more realistic video streams. One could achieve a similar effect using 3D models and rendering them according to the current angle and positions. Second approach would also make it possible to implement a physics engine to go along with the model.

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[1]Srinivasan K. and Vivek S., “Implementation Of Virtual Fitting Room Using Image Processing” IEEE 2017

[2] Ari Kusumaningsih and EkoMulyantoYuniarno, “User Experience Measurement On Virtual Dressing Room Of Madura Batik Clothes”, IEEE 2017

[3] Ting Liu and LingZhi Li, “Real-time 3D Virtual Dressing Based on Users”, IEEE 2017

[4] Naoyuki Yoshino, Stephen Karungaru “Body Physical Measurement using Kinect for Vitual

Dressing Room” 2017 6th IIAI

[5] Dr. Anthony L. Brooks and Dr. EvaPetersson Brooks

[6] Masaki IZUTSU, and Shosiro HATAKEYAMA

“Estimation Method of Clothes Size for Virtual

Fitting Room with Kinect Sensor” 978-1-4799-0652-9/13

[7] OpenNI. http://www.openni.org/

[8] D. Chai, and K. N. Ngan, Face Segmentation using Skin- Color Map in Videophone Applications, IEEE Transactions on Circuits and Systems for Video Technology, vol. 9, no. 4, June 1999.

[9]Kinect Quick Start Guide, http://support.xbox.com/en-

[10] Real time apparel visualization By ShabbirMarzban Mohammad HarisBaig