dai at the mediaeval visual privacy task.pdf

Competence Center Information Retrieval & Machine Learning

DAI at the MediaEval Visual Privacy Task

Dominique Maniry, Esra Acar, Sahin Albayrak

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

Introduction


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.

The Foreground Edges Method (1)


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.



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).



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.

Sample Outputs of the Method (1)


Dropping a bag. Two persons fighting.


823. Juni 2014


923. Juni 2014


1023. Juni 2014

(Objective) Performance Evaluation


(Subjective) Performance Evaluation


Improved Foreground Edges Method (1)


Aim: To improve foreground segmentation.

Main idea: Determine moving edges by the edge-based

foreground segmentation as follows:

The foreground edge segmentation process



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.



With the improved method

Cleaner silhouettes are

obtained, and

False positives are

reduced.

An example output of the improved privacy filter

Methods for MediaEval2014 Visual Privacy Task


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.

Privacy-level based Blurring


Privacy-level based Blurring contains three steps:

Step 1: Blur according to privacy annotation

Step 2: Reduce number of colors

Step 3: Remap colors

Step 1: Blur


Step 2: Reduce Colors


Step 3: Remap Colors


Discussion on Privacy-level based Blurring


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.

Abstract Representation


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.

Unusual Events


Person blobs change to red when an unusual event occurs.

Action is annotated (fighting, stealing or dropping bag).

Discussion on Abstract Representation


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.

Conclusions


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.

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.

[email protected]

Thanks!

013

VideoSense Cluster Workshop20 June 2014

dai at the mediaeval visual privacy task.pdf

Documents

moving edges

videosense cluster workshop0

videosense cluster workshopaim

sign2 significant edges

privacy of people

y gradients

background edge pixels

motion edge detection