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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
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
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 .
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.
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 )
BASIC ADVERTISEMENT STRUCTURE
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
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
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
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.
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)
MATLAB R2009B APPLICATION (DESIGN APPROACH)
MATLAB APPLICATION (INNOVATIVE GUI)
ADVERTISEMENT DETECTION FOR TV VIDEO- LABVIEW 8.6 VI
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
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
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)
AD-KNOWLEDGE MANAGEMENT SYSTEM
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 )
QUESTIONS