Salient event detection in video surveillance s cenarios
Download
1 / 23

- PowerPoint PPT Presentation


  • 265 Views
  • Uploaded on

Salient event detection in video surveillance s cenarios. Kenneth Ellingsen Master’s thesis presentations - 05.06.2008. Supervisor: Faouzi Alaya Cheikh, Dr. Tech. Department of Computer Science and Media Technology Gjøvik University College, Norway. Outline. Introduction Abnormal events

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about '' - dewey


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Salient event detection in video surveillance s cenarios l.jpg

Salient event detection in video surveillance scenarios

Kenneth Ellingsen

Master’s thesis presentations - 05.06.2008

Supervisor: Faouzi Alaya Cheikh, Dr. Tech.

Department of Computer Science and Media Technology

Gjøvik University College, Norway


Outline l.jpg
Outline

  • Introduction

  • Abnormal events

  • Proposed system

  • Event classification

  • Feature extraction

  • Feature analysis

  • Results

  • Conclusions


Introduction l.jpg
Introduction

  • Large amounts of surveillance data

    accumulate each day.

  • Monitored by very few observers relative to the number of cameras which makes it impossible to detect and respond to all abnormal event when they occur.

  • Propose a system for automatic detection of abnormal events in video surveillance scenarios.


Introduction4 l.jpg
Introduction

  • Goal is to extract simple and reliable features which are descriptive and that can be used by an unsupervised algorithm to discover the important and unusual events.

  • Examine the possibility of modeling abnormal events.

  • Analyze objects behaviors in video sequences

    over time.

  • Define criteria’s that characterizes the event.

  • Compare features against predefined criteria’s.


Outline5 l.jpg
Outline

  • Introduction

  • Abnormal events

  • Proposed system

  • Event classification

  • Feature extraction

  • Feature analysis

  • Results

  • Conclusions


Abnormal events l.jpg
Abnormal events

  • Abnormal events are something that deviates from the normal behavior. What is abnormal?

    • Unpredictable behavior.


Abnormal events7 l.jpg
Abnormal events

  • Types of events:

    • Chasing

    • Exchange of objects

    • Fighting

    • Loading/unloading

    • Object dropping

    • Sneaking

    • Stealing

  • Focus on the event of object dropping in public places such as airports and train stations etc.


Outline8 l.jpg
Outline

  • Introduction

  • Abnormal events

  • Proposed system

  • Event classification

  • Feature extraction

  • Feature analysis

  • Results

  • Conclusions


Proposed system l.jpg
Proposed system

  • System overview

  • Four main blocks:

    • Background estimation

    • Object tracking

    • Feature extraction

    • Feature analysis


Outline10 l.jpg
Outline

  • Introduction

  • Abnormal events

  • Proposed system

  • Event classification

  • Feature extraction

  • Feature analysis

  • Results

  • Conclusions


Event classification l.jpg
Event classification

  • Object dropping

    • Subjective analysis of several surveillance datasets.

    • Derive a general description of object behavior

      during the event.

  • The extracted low-level features:

    • Area

    • Center of mass

    • Displacement information

    • Width-height-ratio

    • Numel

    • Minor axis


Event classification12 l.jpg
Event classification

  • Object dropping criteria’s


Outline13 l.jpg
Outline

  • Introduction

  • Abnormal events

  • Proposed system

  • Event classification

  • Feature extraction

  • Feature analysis

  • Results

  • Conclusions


Feature extraction l.jpg
Feature extraction

  • Extract and save feature data of all object for each frame.

  • Filter feature data to remove noise elements.

  • Sort feature data to obtain correct labeling of objects.

  • Plotting of data for visual analysis.


Experiments l.jpg
Experiments

  • Example plots

Directional information (x-axis)

Numel

Center of mass (x-axis)

Center of mass (y-axis)


Outline16 l.jpg
Outline

  • Introduction

  • Abnormal events

  • Proposed system

  • Event classification

  • Experiments

  • Feature analysis

  • Results

  • Conclusions


Feature analysis l.jpg
Feature analysis

  • The analysis-stage is triggered by the Numel-feature.

  • One feature by itself is not conclusive enough to determine an object dropping.

  • A combination of the features has to be taken into consideration.

  • Some features need to be examined over a time period.


Feature analysis18 l.jpg
Feature analysis

  • Object dropping classifier:

input video

search window = 2 x framerate(video)

for each frame

if (numel increase by 1 and numel >= 2)

Area = true when

‘Significant drop in size of first object at current frame’

‘No significant size increase in search window’

‘Second objects size equal to first object size drop’

Center of mass= true when

’Distance between first and second object is less then 2 x Minoraxis’

Ratio = true when

‘Increase for the first object before drop within search window’

‘Highest ratio near point of first objects standstill’

Directional information = true when

‘Find first objects approx. standstill in search window’

‘Check first objects translation history in search window from point

if it of standstill gradually increase until drop is made’

if (all return true)

object drop has occurred

else

no drop has occurred

end

end

end


Outline19 l.jpg
Outline

  • Introduction

  • Abnormal events

  • Proposed system

  • Event classification

  • Experiments

  • Feature analysis

  • Results

  • Conclusions


Results l.jpg
Results

  • Object dropping videos

    • Table with results from analysis-stage.

    • Video 2 shown below.


Outline21 l.jpg
Outline

  • Introduction

  • Abnormal events

  • Proposed system

  • Event classification

  • Experiments

  • Feature analysis

  • Results

  • Conclusions


Conclusions l.jpg
Conclusions

  • We were able to model object dropping, by:

    • Subjective analysis of video data.

    • Making a general description of the event.

    • Define a set of criteria’s.

    • Extracting simple features from object.

  • Based on the event classification the system managed to detect the points of the object dropping.



ad