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

Outline  Introduction  Methods  Evaluation  Conclusions 2

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

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

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

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

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

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

Introduction 9  Markov Model

Introduction 10  Hidden Markov Model

Methods 11

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.

Methods 13

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

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

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

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

Methods 18

Methods 19

Methods 20

Methods 21 More sensitive Less sensitive

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

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

Methods 24

Methods 25

Methods 26

Methods 27

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

Evaluation 29

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

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

Thanks for your attention 32