Results on video summarization

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The document discusses a video summarization framework that includes shot boundary detection, video sequence classification, detection of talking head shots, detection of day and night shots, and video quality indicators. It also discusses recognizing events, indexing over 5,000 videos with over 27 million frames, developing summarization components, and evaluating summarization algorithms with collaborators.

Science

“Results on Video Summarization”
Mikołaj Leszczuk, Michał Grega, Jan Derkacz
2017-04-28

Video Summarization
Framework Work-Flow
2

Shot Boundary Detection
(SBD)
» Based on
Py-Scene-
Detect
» Integrated
3

Classification of
Video Sequences
VideoCategories
A
(News Report)
B
(Discussion)
C
(Video Stream)
» Based on pattern
of emerging faces
» Technique of
Hidden Markov
Models
» Pending
4

Detection of “Talking Head”
Shots (1/2)
» Based on Mouth
Region of Interest
processing
» Processed shot-by-
shot
5
Face detection
Mouth
movement
detection
Cascade
classifier
Not Talking HeadTalking Head

Detection of “Talking Head”
Shots (2/2)
» Face detection using Haar Cascades
» Sensitivity 88%, Specificity 100%
» Integrated
6

Detection of Day & Night
Shots
» Based on
neural
network
» Tested on
>2000
photos
» Efficiency
>90%
» Integrated
7

Video Quality Indicators
» Video quality
assessment system for
video sequences
» Quality of Experience
(QoE)
» 13 quality parameters
» Temporal Activity (TA)
» Spatial Activity (SA)
» Integrated
8

Recognition Events for Purpose
of Summarizing Video Sequences
» Creation &
implementation of
algorithms to recognize
motions/gestures &
other events in video
sequences
» Pending
9
By Comixboy at English Wikipedia, CC BY 2.5,
https://commons.wikimedia.org/w/index.php?curid=9672553

Database Statistics
» Number of videos indexed – 5423
» Number of frames indexed – 27 384 115
» Features indexed:
– Shot Boundary Detection
– 13 Video Quality Indicators
– Spatial Activity
– Temporal Activity
» Features pending (expected May 2017):
– Automatic Speech Recognition
– Day/Night
10

1st Version of Content Analysis &
Video Summarization Components
11
0
20
40
60
80
100
120
1
129
257
385
513
641
769
897
1025
1153
1281
1409
1537
1665
1793
1921
2049
2177
2305
2433
2561
2689
2817
2945
3073
3201
3329
3457
3585
3713
3841
3969
4097
4225
4353
4481
4609
4737
4865
4993
5121
5249
5377
5505
5633
5761
5889
6017
6145
6273
6401
6529
6657
6785
6913
7041
7169
7297
7425
7553
7681
7809
7937
Activity
Frame Number
5KPk3rkESlU
Spatial Activity Temporal Activity

Evaluation of Multimedia
Content Summarisation Algorithms
» Together with DEUSTO
» Review of State-of-the-Art
» Collaboration with
Video Quality Experts Group
– Project: Quality Assessment
for Recognition and Task-
based multimedia
applications (QART)
– Meeting in May 2017
» Pending
15

For the full video of this presentation, please visit: http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/dec-2016-member-meeting-uofw For more information about embedded vision, please visit: http://www.embedded-vision.com Professor Jeff Bilmes of the University of Washington delivers the presentation "Image and Video Summarization" at the December 2016 Embedded Vision Alliance Member Meeting. Bilmes provides an overview of the state of the art in image and video summarization.

Mtech Second progresspresentation ON VIDEO SUMMARIZATION

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Keyframe-based Video Summarization Designer

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https://imatge.upc.edu/web/publications/keyframe-based-video-summarization-designer This Final Degree Work extends two previous projects and consists in carrying out an improvement of the video keyframe extraction module from one of them called Designer Master, by integrating the algorithms that were developed in the other, Object Maps. Firstly the proposed solution is explained, which consists in a shot detection method, where the input video is sampled uniformly and afterwards, cumulative pixel-to-pixel difference is applied and a classifier decides which frames are keyframes or not. Last, to validate our approach we conducted a user study in which both applications were compared. Users were asked to complete a survey regarding to different summaries created by means of the original application and with the one developed in this project. The results obtained were analyzed and they showed that the improvement done in the keyframes extraction module improves slightly the application performance and the quality of the generated summaries.

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With the increasing in the number of anti-social activates that have been taking place, security has been given utmost importance lately. Many Organizations have installed CCTVs for constant Monitoring of people and their interactions. For a developed Country with a population of 64 million, every person is captured by a camera 30 times a day. A lot of video data generated and stored for a certain time duration. A 704x576 resolution image recorded at 25fps will generate roughly 20GB per day. Constant Monitoring of data by humans to judge if the events are abnormal is near impossible task as requires a workforce and their constant attention. This creates a need to automate the same. Also , there is need to show in which frame and which part of it contain the unusual activity which aid the faster judgment of the unusual activity being abnormal. This is done by converting video into frames and analyzing the persons and their activates from the processed frame .Machine learning and Deep Learning Algorithms and techniques support us in a wide accept to make Possible.

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Results on video summarization

1. “Results on Video Summarization” Mikołaj Leszczuk, Michał Grega, Jan Derkacz 2017-04-28

2. Video Summarization Framework Work-Flow 2

3. Shot Boundary Detection (SBD) » Based on Py-Scene- Detect » Integrated 3

4. Classification of Video Sequences VideoCategories A (News Report) B (Discussion) C (Video Stream) » Based on pattern of emerging faces » Technique of Hidden Markov Models » Pending 4

5. Detection of “Talking Head” Shots (1/2) » Based on Mouth Region of Interest processing » Processed shot-by- shot 5 Face detection Mouth movement detection Cascade classifier Not Talking HeadTalking Head

6. Detection of “Talking Head” Shots (2/2) » Face detection using Haar Cascades » Sensitivity 88%, Specificity 100% » Integrated 6

7. Detection of Day & Night Shots » Based on neural network » Tested on >2000 photos » Efficiency >90% » Integrated 7

8. Video Quality Indicators » Video quality assessment system for video sequences » Quality of Experience (QoE) » 13 quality parameters » Temporal Activity (TA) » Spatial Activity (SA) » Integrated 8

9. Recognition Events for Purpose of Summarizing Video Sequences » Creation & implementation of algorithms to recognize motions/gestures & other events in video sequences » Pending 9 By Comixboy at English Wikipedia, CC BY 2.5, https://commons.wikimedia.org/w/index.php?curid=9672553

10. Database Statistics » Number of videos indexed – 5423 » Number of frames indexed – 27 384 115 » Features indexed: – Shot Boundary Detection – 13 Video Quality Indicators – Spatial Activity – Temporal Activity » Features pending (expected May 2017): – Automatic Speech Recognition – Day/Night 10

11. 1st Version of Content Analysis & Video Summarization Components 11 0 20 40 60 80 100 120 1 129 257 385 513 641 769 897 1025 1153 1281 1409 1537 1665 1793 1921 2049 2177 2305 2433 2561 2689 2817 2945 3073 3201 3329 3457 3585 3713 3841 3969 4097 4225 4353 4481 4609 4737 4865 4993 5121 5249 5377 5505 5633 5761 5889 6017 6145 6273 6401 6529 6657 6785 6913 7041 7169 7297 7425 7553 7681 7809 7937 Activity Frame Number 5KPk3rkESlU Spatial Activity Temporal Activity

12. Demonstration (Original) 12

13. Demonstration (Summarised) 13

14. Memes – Updated Schema 14

15. Evaluation of Multimedia Content Summarisation Algorithms » Together with DEUSTO » Review of State-of-the-Art » Collaboration with Video Quality Experts Group – Project: Quality Assessment for Recognition and Task- based multimedia applications (QART) – Meeting in May 2017 » Pending 15

Editor's Notes

Face should be large enough (3% of the scene size) One face only (90% of the frames in scene with 1 face) Open/closed ratio 20% or higher
We have a video of a real event, for example the election in France. The entire recording is 15 minutes, we want to shorten it to 1.5 minutes. Algorithm cuts and processes video. Now we want to compare how much content from the original video got into the summary. Someone (some researchers from the project) watch these 15 mins and they make a summary, they tell the most important thing they learned. It would be good if they were journalists, not engineers. Now we can ask people to write down what they learned from summaries and do text mining, or this is true of the facts described by professionals, or we can ask to generate questions by specialists and taking a viewing test. In each of these cases we have the problem of knowing before looking at a summary that needs to be addressed in some way.

Results on video summarization

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Editor's Notes