Salient event detection in video surveillance s cenarios

1. 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

2. Outline • Introduction • Abnormal events • Proposed system • Event classification • Feature extraction • Feature analysis • Results • Conclusions

3. 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.

4. 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.

5. Outline • Introduction • Abnormal events • Proposed system • Event classification • Feature extraction • Feature analysis • Results • Conclusions

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

7. 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.

8. Outline • Introduction • Abnormal events • Proposed system • Event classification • Feature extraction • Feature analysis • Results • Conclusions

9. Proposed system • System overview • Four main blocks: • Background estimation • Object tracking • Feature extraction • Feature analysis

10. Outline • Introduction • Abnormal events • Proposed system • Event classification • Feature extraction • Feature analysis • Results • Conclusions

11. 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

12. Event classification • Object dropping criteria’s

13. Outline • Introduction • Abnormal events • Proposed system • Event classification • Feature extraction • Feature analysis • Results • Conclusions

14. 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.

15. Experiments • Example plots Directional information (x-axis) Numel Center of mass (x-axis) Center of mass (y-axis)

16. Outline • Introduction • Abnormal events • Proposed system • Event classification • Experiments • Feature analysis • Results • Conclusions

17. 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.

18. 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

19. Outline • Introduction • Abnormal events • Proposed system • Event classification • Experiments • Feature analysis • Results • Conclusions

20. Results • Object dropping videos • Table with results from analysis-stage. • Video 2 shown below.

21. Outline • Introduction • Abnormal events • Proposed system • Event classification • Experiments • Feature analysis • Results • Conclusions

22. 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.

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