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Competence Center Information Retrieval & Machine Learning
DAI at the MediaEval Visual Privacy Task
Dominique Maniry, Esra Acar, Sahin Albayrak
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Outline
220 June 2014 VideoSense Cluster Workshop
Introduction
The Foreground Edges Method
Sample Outputs of the Method
Objective & Subjective Evaluation
Improved Foreground Edges Method
Methods for the MediaEval2014 Visual Privacy Task
Privacy-level based Blurring
Abstract Representation
Conclusions
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Introduction
320 June 2014 VideoSense Cluster Workshop
MediaEval Visual Privacy Task (VPT) aims at developing solutions to ensure that the privacy of people in videos is protected.
Running since 2012 within the MediaEval workshop.
Object detections are given in 2013.
The focus of the task is to make persons appearing in videos unrecognizable.
Evaluation is performed using the PEViD dataset
consists of about 60 high resolution video files of an average length of 20 seconds each.
contains both indoors and outdoors scenarios (including night-time videos)
The people shown in the videos perform various actions, such as exchanging objects, talking, fighting or simply walking by.
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The Foreground Edges Method (1)
420 June 2014 VideoSense Cluster Workshop
The face is NOT the only body part which can disclose the
identity of an individual.
Main idea: To replace whole bodies by silhouettes defined by
moving edges.
Based on motion edge detection.
Foreground edges within a person's bounding box, is set to
green.
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The Foreground Edges Method (2)
520 June 2014 VideoSense Cluster Workshop
0 no significant edges
1 significant edges with the same sign
2 significant edges with different signs
Apply horizontal and vertical Sobel masks (Ex(x,y), and Ey(x,y))
and quantize the edge results (E(x,y)) to one of three levels {0,
1,2}.
Determine frame differences by comparing E(x, y, t) with
background edge pixels B(x, y, t).
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The Foreground Edges Method (3)
620 June 2014 VideoSense Cluster Workshop
Pixels with frame difference values of 2 are considered as
foreground.
Background image is updated regularly after the initialization
The time constant, 0 < < 1.0, controls the speed of foreground
pixel classification change to background.
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Sample Outputs of the Method (1)
720 June 2014 VideoSense Cluster Workshop
Dropping a bag. Two persons fighting.
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Sample Outputs of the Method (2)
823. Juni 2014
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Sample Outputs of the Method (3)
923. Juni 2014
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Sample Outputs of the Method (4)
1023. Juni 2014
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(Objective) Performance Evaluation
1120 June 2014 VideoSense Cluster Workshop
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(Subjective) Performance Evaluation
1220 June 2014 VideoSense Cluster Workshop
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Improved Foreground Edges Method (1)
1320 June 2014 VideoSense Cluster Workshop
Aim: To improve foreground segmentation.
Main idea: Determine moving edges by the edge-based
foreground segmentation as follows:
The foreground edge segmentation process
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Improved Foreground Edges Method (2)
1420 June 2014 VideoSense Cluster Workshop
A long-term and a short-term background model are used.
The short-term model and long-term models have different
learning rates.
The x and y gradients
are modelled independently, and
later combined using a thresholding on foreground edges (as
in the Canny edge detection).
In order to control the level of detail in constructed silhouettes,
we employ adaptive thresholds in the method.
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Improved Foreground Edges Method (3)
1520 June 2014 VideoSense Cluster Workshop
With the improved method
Cleaner silhouettes are
obtained, and
False positives are
reduced.
An example output of the improved privacy filter
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Methods for MediaEval2014 Visual Privacy Task
1620 June 2014 VideoSense Cluster Workshop
The VPT task of 2014 puts the emphasis on the human point of
view on privacy.
Only human viewers can determine whether privacy is
protected or not.
The VPT task introduces different insights on what an effective
privacy protection should feature.
General public from online communities,
Video surveillance staff as trained CCTV monitoring
professionals, and
Focus group comprising video-analytics technology and
privacy protection solutions developers.
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Privacy-level based Blurring
1720 June 2014 VideoSense Cluster Workshop
Privacy-level based Blurring contains three steps:
Step 1: Blur according to privacy annotation
Step 2: Reduce number of colors
Step 3: Remap colors
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Step 1: Blur
1820 June 2014 VideoSense Cluster Workshop
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Step 2: Reduce Colors
1920 June 2014 VideoSense Cluster Workshop
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Step 3: Remap Colors
2020 June 2014 VideoSense Cluster Workshop
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Sample Outputs of the Method (1)
2120 June 2014 VideoSense Cluster Workshop
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Sample Outputs of the Method (2)
2220 June 2014 VideoSense Cluster Workshop
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Sample Outputs of the Method (3)
2320 June 2014 VideoSense Cluster Workshop
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Discussion on Privacy-level based Blurring
2420 June 2014 VideoSense Cluster Workshop
Pros Cons
Parameters to tune trade-off between
privacy and intelligibility (blur intensity
and number of colors).
Remapped colors can convey
additional information.
Different regions can have different
privacy levels by using different blur
intensities (e.g. face blurred more
than full body).
Simple.
Identity related details can leak
through shape.
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Abstract Representation
2520 June 2014 VideoSense Cluster Workshop
This years annotations simulate perfect action recognition.
Idea: Completely replace persons with an abstract
representation and display actions using color and overlays.
Re-render relevant objects on background model and annotate
if necessary.
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Unusual Events
2620 June 2014 VideoSense Cluster Workshop
Person blobs change to red when an unusual event occurs.
Action is annotated (fighting, stealing or dropping bag).
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Discussion on Abstract Representation
2720 June 2014 VideoSense Cluster Workshop
Pros Cons
Maximum privacy. Person representation can be
unintuitive.
Needs a background model.
Would need a fallback in a real system
based on the confidence of action
recognition.
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Conclusions
2820 June 2014 VideoSense Cluster Workshop
The user study shows that the basic foreground edges filter is
able to provide privacy while maintaining intelligibility.
We initialize the background using the first frame of a video.
The first frame of a video might already contain an individual.
The improved foreground edge filter led to cleaner silhouettes
and reduced false positives.
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Competence Center Information Retrieval &
Machine Learning
www.dai-labor.de
Fon
Fax
+49 (0) 30 / 314 74
+49 (0) 30 / 314 74 003
DAI-Labor
Technische Universitt Berlin
Fakultt IV Elektrontechnik & Informatik
Sekretariat TEL 14
Ernst-Reuter-Platz 7
10587 Berlin, Deutschland
29
Esra Acar
Researcher
M.Sc.
Thanks!
013
VideoSense Cluster Workshop20 June 2014