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EFFECTS OF THRESHOLD OF HARD CUT BASED TECHNIQUE FOR ADVERTISEMENT DETECTION IN TV VIDEO STREAMS Presented By Ashish Tanwer (Student Member, IEEE) & Mr. Parminder Singh Reel (Member, IEEE) THAPAR UNIVERSITY, PATIALA Electronics and Communication Engineering Department

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Page 1: Ieee tech sym

EFFECTS OF THRESHOLD OF HARD CUT BASED TECHNIQUE FOR

ADVERTISEMENT DETECTION IN TV VIDEO STREAMS

Presented By

Ashish Tanwer (Student Member, IEEE) &

Mr. Parminder Singh Reel (Member, IEEE)

THAPAR UNIVERSITY, PATIALA

Electronics and Communication Engineering Department

Page 2: Ieee tech sym

CONTENTS INTRODUCTION & OBJECTIVE

TECHNICAL FEATURES OF ADVERTISEMENT BLOCK

VIDEO ANALYSIS OF ADVERTISEMENT MEDIA

EXPERIMENTAL ANALYSIS

FLOW PROCESS OF ADVERTISEMENT DETECTION

DESIGNED MATLAB R2009b GUI AND LABVIEW 8.6 VI

PRACTICAL APPLICATIONS

AD-KNOWLEDGE MANAGEMENT SYSTEM

FUTURE WORK

Page 3: Ieee tech sym

INTRODUCTION Advertisements are boon as well as curse. TV viewer faces a frustrating problem of viewing vast

number of advertisements Advertisements are needed to be monitored by

broadcasters/advertisement agencies and censoring bodies.

Automatically detecting advertisements is a challenging task.

No Hardware/Software to detect advertisements available in market .

Page 4: Ieee tech sym

OBJECTIVE We have designed and implement hardware software co-

designed architecture that can detect, identify and firewall

advertisements automatically from TV videos.

It is able to work for all types of videos (i.e. News, Sports,

Movies, Cartoons).

It exploits basic characteristics of ads to detect them that

can never be changed by Advertisement Companies.

Page 5: Ieee tech sym

TECHNICAL FEATURES OF ADVERTISEMENT BLOCK

Some Measurable [Sure shot] features are: Repeated Video Sequence Restricted temporal length (generally between 10-60

sec ) Two consecutive ads are separated by 5-10 dark blank

frames (for audio separation and avoiding video interleaving)

High Hard Cut Rate (Average 20.9 CPM against 3.7 CPM for normal video )

Absence of Correlation between video frames (due to camera and viewpoint change )

Page 6: Ieee tech sym

BASIC ADVERTISEMENT STRUCTURE

Page 7: Ieee tech sym

ADVERTISEMENT DETECTION PROCESS IN TV VIDEO STREAMS

10s<d<60s

Streaming Media

Adv1 Intro Adv N

Dark frames

Media Intro

Advertisements

Black Frames

Desired Media

Transmitted Media

Page 8: Ieee tech sym

VIDEO ANALYSIS OF ADVERTISEMENT MEDIA

Shot change detection can be performed by comparing successive frames.

Global inter-frame difference measure as

M and N are the number of rows and column respectively. The size of a frame is calculated by multiply both M and N. First frame is denoted by I1.

1 1 1 1

( 1, , ) ( 2, , )M N M N

i j i j

P I i j P I i j K

Page 9: Ieee tech sym

METHODOLOGY (VIDEO ANALYSIS)

Streaming Video

Correlation Coefficient (R)

If Correlation Coefficient (R) < Threshold-

2(K)

If Correlation Coefficient (R) < Threshold-

2(K)

Candidate Hard-CutCandidate Hard-Cut

Main Hard-CutMain Hard-Cut

ThresholdVideo Info

Hard-Cut False Hard-Cut Check

+

If Correlation Coefficient (R) < Threshold-

1(T)

If Correlation Coefficient (R) < Threshold-

1(T)

Yes

No

Page 10: Ieee tech sym

EXPERIMENTAL ANALYSIS-I The methodology described above is analyzed and

implemented on four TV Channels offering video streams.

Our GUI and VI can perform Hard-Cut Detection in half an hour

segment of TV video . The recordings were such chosen that

they consisted of video with at least one ad-break.

Figure (next slide) exhibits the effects of threshold value K. An

optimised value of threshold is realised by analysing the

precision and recall metrics.

Table (second next slide) shows the results achieved with

optimised values of threshold K and true location of the ad-

breaks within each segment of video.

Page 11: Ieee tech sym

EXPERIMENTAL ANALYSIS-IIPrecision & Recall Confusion matrix

FPTP

TPP

FNTP

TPR

TV Channelof clip

Precision (%)

Recall (%)

(a) National 99.3 99.6

(b) DD Sports 99.1 99.8

(c) DD News 99.7 99.5

(d) Star Utsav 99.7 99.7

Classifier Instance (Positive) Instance (Negative)

Positive True Positives (TP) False Positives (FP)

Negative False Negatives (FN) True Negatives (TN)

Page 12: Ieee tech sym

MATLAB R2009B APPLICATION (DESIGN APPROACH)

Page 13: Ieee tech sym

MATLAB APPLICATION (INNOVATIVE GUI)

Page 14: Ieee tech sym

ADVERTISEMENT DETECTION FOR TV VIDEO- LABVIEW 8.6 VI

Page 15: Ieee tech sym

EXPERIMENTAL ANALYSIS-III

4 Channels used for Analysis:

TV Channelof clip

Length of clipNo of Frames (Mins)

Ad-breakdetected(frame - frame)

True ad-breaklocation(frame-frame)

(a) XY National 43000 frames (30mins) 5795 - 8435 5794 – 8445

(b) YZ Sports 43000 frames (30mins) 2829 - 8777 2819 - 8780

(c) AB News 43000 frames (30mins) 2507 - 5054 2512 – 5052

(d) CD Cartoon 43000 frames (30mins) 4923 - 7261 4923 – 7262

Page 16: Ieee tech sym

If Service is

premium?

Is Video Real-Time?

Is there any Ad?

Is there any Ad?

Is Ad in Database index?

Is Ad being transmitted at this

time?

OK

VIDEO INPUT

Show Videos as it is ( i.e. with predefined broadcasters ads)

Show only valid /non expired Ads

No Ads only Video contents

No

Check for status and

privilege of Ad-firewall user

Ask for indexing from

user/operator

Ask for indexing from

user/operator Check for the subscribed list

of the user

Ok

Ok

Check RatingCheck Rating

Yes

No

No

No

Yes

Yes

Yes

No

Yes

Standard User

Show user something else as per their choice

FLOW PROCESS OF ADVERTISEMENT DETECTION

Page 17: Ieee tech sym

PRACTICAL APPLICATIONS OF OUR WORK

Advertisement firewall for IP-TV/PVR at client end.

Ad-Knowledge Management system for Network based PVR

server.

For Archiving Indexed values without Advertisements.

For observing/feedback of subscriber (i.e. Customers want to see

more local ads. Branded Ads, Funny Ads, Violent Ads, Ads With

Animals etc)

Page 18: Ieee tech sym

AD-KNOWLEDGE MANAGEMENT SYSTEM

Page 19: Ieee tech sym

FUTURE WORK

Our future work will be on Video Advertisement Watermarking

that can be used to embed the following information inside the

video:

Name of Publisher/Maker/Advertiser

Genre/ Type /Rating (Funny, rude, Humorous, Stupid and not

suitable for kids)

Keywords for Indexing (i.e. Sports, Health-Care, Fast-Food,

General)

Validity /Expiry (i.e. time bounded ads with dates )

Page 20: Ieee tech sym

QUESTIONS