Transcript 下載/瀏覽Download

Using Inactivity to Detect
Unusual behavior
Dickinson, P.; Hunter, A.
Motion and video Computing, 2008. WMVC 2008. IEEE
Workshop on, Issue Date: 8-9 Jan. 2008 , Page(s): 1 - 6
Presenter : Siang Wang
Advisor :
Dr. Yen - Ting Chen
Date :
2014.11.26
2
Outline
Introduction
Methods
Evaluation
Conclusions
3
Introduction
Automated visual surveillance systems are
often intended to detect interesting, unusual
or abnormal activity in a monitored scene.
4
Introduction
Interest has develop around the potential of
automated surveillance to support homebased care of peoples.
Elderly
Vulnerable people
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.
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
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.
8
Introduction
A probabilistic spatial map of inactivity
mixture of Gaussians (MoG) in 2 dimensional
space
Conjunction with Hidden Markov Model
(HMM) framework
9
Introduction
Markov Model
10
Introduction
Hidden Markov Model
11
Methods
Foreground segmentation technique
The corresponding eigenvectors
1
𝑒𝑡
and
2
𝑒𝑡
An oriented bounding box (OBB) around the
silhouette
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
Detect trajectory start and end points by
calculating a mean target velocity,𝑣𝑡 , for
each image frame, over a time window w
(1)
𝑇𝑖𝑛 , 𝑉𝑖𝑛 , 𝑉𝑚𝑣 represent the only
configurable parameters for our system
14
Methods
The probability of observing some trajectory
end point xi is then given by :
15
Methods
Expectation Maximization (EM) algorithm
E-step : the expectation step calculates the
posterior probability
16
Methods
Expectation Maximization (EM) algorithm
M-step : The model parameters are then reestimated from the statistics of the training data
17
Methods
Expectation Maximization (EM) algorithm
M-step : The model parameters are then reestimated from the statistics of the training data
18
Methods
Different values for the configurable
parameters 𝑇𝑖𝑛 , 𝑉𝑖𝑛 and 𝑉𝑚𝑣
19
Methods
20
Methods
21
Methods
Different values for the configurable
parameters 𝑇𝑖𝑛 , 𝑉𝑖𝑛 and 𝑉𝑚𝑣
More sensitive
𝑉𝑖𝑛 > ( 𝑇𝑖𝑛 or 𝑉𝑚𝑣 )
Less sensitive
𝑉𝑖𝑛 < ( 𝑇𝑖𝑛 or 𝑉𝑚𝑣 )
22
Methods
The Hidden Markov Model Framework
First :
Parameter
𝜆𝑛𝑚
normal sequences of inactivity
events
𝜃𝑜𝑝𝑡
the inactivity map
𝜒𝑗
the training data , j = {0 . . . 𝑁𝑠 }
23
Methods
The Hidden Markov Model Framework
Second :
Parameter
λth
inferred threshold model
X𝑜𝑏𝑠
detect unusual sequences by
comparing the model likelihoods
over
24
Methods
𝜋𝑘 is the probability of a sequence
initializing in state 𝑞𝑘 in 𝜆𝑛𝑚
𝑋𝑗 (1) is the first data point in sequence 𝑋𝑗
25
Methods
The transition probabilities A between the
states are constructed in similar fashion
𝑁𝑥 is the number of data points in sequence
𝑋𝑗
26
Methods
The Threshold Model
is identical 𝜆𝑛𝑚 apart from the transition matrix
A
27
Methods
The trajectory end points 𝑋𝑜𝑏𝑠 are extracted
using equation 1, with same values of 𝑇𝑖𝑛 ,
𝑉𝑖𝑛 , and 𝑉𝑚𝑣
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
29
Evaluation
30
Conclusion
2D MoG model
Learned using EM
Build a pair of HMMs
normal sequences of inactivity
arbitrary behavior
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
32
Thanks for your attention