1. Using Inactivity to Detect Unusual behavior Presenter : Siang Wang Advisor : Dr. Yen - Ting Chen Date : 2014.11.26 Motion and video Computing, 2008. WMVC 2008. IEEE Workshop on, Issue Date: 8-9 Jan. 2008, Page(s): 1 - 6 Dickinson, P.; Hunter, A.

2. Outline  Introduction  Methods  Evaluation  Conclusions 2

3. Introduction  Automated visual surveillance systems are often intended to detect interesting, unusual or abnormal activity in a monitored scene. 3

4. Introduction  Interest has develop around the potential of automated surveillance to support home- based care of peoples.  Elderly  Vulnerable people 4

5. Introduction  The costs associated with providing traditional methods of assistive care to ageing populations is rising, and set to rise much further in the future.  The applications of surveillance in residential care homes have been considered. 5

6. Introduction  The detection of unusual patterns of behavior  Anomalies indicate a requirement for intervention by a care provider.  Developing a “transparent” model which is readily interpretable 6

7. Introduction  Behavior recognition are largely based on learning time-series models of specific activities.  The observation is about the behavior of a person in their home, and particularly that of an elderly person, is fairly sedentary. 7

8. Introduction  A probabilistic spatial map of inactivity  mixture of Gaussians (MoG) in 2 dimensional space  Conjunction with Hidden Markov Model (HMM) framework 8

9. Introduction 9  Markov Model

10. Introduction 10  Hidden Markov Model

11. Methods 11

12. Methods 12 S. McKenna and H. Nait-Charif. Summarising contextual activity and detecting unusual inactivity in a supportive home environment. Pattern Analysis and Applications, 7(4):386 – 401, 2004.

13. Methods 13

14. Methods  The probability of observing some trajectory end point x i is then given by : 14

15. Methods  Expectation Maximization (EM) algorithm  E-step : the expectation step calculates the posterior probability 15

16. Methods  Expectation Maximization (EM) algorithm  M-step : The model parameters are then re- estimated from the statistics of the training data 16

17. Methods  Expectation Maximization (EM) algorithm  M-step : The model parameters are then re- estimated from the statistics of the training data 17

18. Methods 18

19. Methods 19

20. Methods 20

21. Methods 21 More sensitive Less sensitive

22. Methods  The Hidden Markov Model Framework First : 22 Parameter normal sequences of inactivity events the inactivity map

23. Methods  The Hidden Markov Model Framework Second : 23 Parameter inferred threshold model detect unusual sequences by comparing the model likelihoods over

24. Methods 24

25. Methods 25

26. Methods 26

27. Methods 27

28. Evaluation  Filmed two sets of test sequences for each scene  Comprised small variations on the scripted activities  Displayed the types of unusual or abnormal behaviors 28

29. Evaluation 29

30. Conclusion  2D MoG model  Learned using EM  Build a pair of HMMs  normal sequences of inactivity  arbitrary behavior 30

31. Conclusion The advantage of the proposed system  is not dependent on identifying specific activities  is tolerant to small variations in normal behavior  is unsupervised and having only a few configurable parameters 31

32. Thanks for your attention 32