Ziming
Zhang,
Yucheng
Zhao and
Yiwen
Wan
Introduction
&Motivation
Problem Statement
Paper Summeries
Discussion and Conclusions
Anomaly is a pattern in the data that
does not conform to the expected
behaviour
Also referred to as outliers, exceptions,
peculiarities, surprise, etc.
outlier
outlier
Vehicle behavior is represented as trajectories
When trajectory does conform to dominant
pattern it is detected as anomaly or outlier
Collective Anomalies
Data Input:
Spatio

temperal
trajectories of moving objects
Scene Modeling:
•
Scene Representation: interest points/path
•
Learning
Model:unsupervised
/supervised
Activity Analysis: virtual fencing, speed
profiling, path classification,
anomaly
detection, online activity analysis and
object interaction characterization
Accurate and efficient representation of trajectories
Defining
a representative normal
pattern is
challenging
The boundary between normal and outlying
behaviour
is often not precise
Availability
of
labelled
data
for training/validation
Data
might contain noise
Normal
behaviour
keeps evolving
Cláudio
Rosito
Jung
, Member, IEEE,
Luciano
Hennemann
, and
Soraia
Raupp
Musse
IEEE TRANSACTIONS ON CIRCUITS AND
SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 18,
NO. 11, NOVEMBER 2008
Representation
Input Trajectories
Initial Clustering
Cluster
Representation using
4

D histogram
Event Detection
(x1,y1)
(x2,y2)
(
xn,yn
)
(x3,y3)
……
F=(x1

x2,y1

y2,x2

x3,y2

y3…xn

xn

1,yn

yn

1)
Representation
Input Trajectories
Initial Clustering
Cluster
Representation using
4

D histogram
Event Detection
Representation
Input Trajectories
Initial Clustering
Cluster
Representation using
4

D histogram
Event Detection
Representation
Input Trajectories
Initial Clustering
Cluster
Representation using
4

D histogram
Event Detection
trajectories collected from trackers
Offline clustering based on Mixture of Gaussian is
used for path modeling
4

D histogram is used to represent spatial and
temporal characteristics of each cluster/path for
further event detection such as drift, shift, entry,
bifurcation, confluence, incoherent local speed,
incoherent local orientation pattern
Two dataset(pedestrian and traffic scenario) are
tested and 20 human observers were used for
accuracy validation: the number of evaluation
that agreed with results from proposed method
Nicolas
Saunier
and
Tarek
Sayed
Reduction of public resources on
detecting traffic collision.
Conflicting causes collisions
Conflicting definition
›
Two or more vehicles closed enough in time
and space
Trajectory representation
›
A sequence of {x, y,
v
x
,
v
y
}
HMM (Hidden Markov Model)
HMM (Hidden Markov Model)
›
Sequence of observation = {walk, shop,
clean}
›
Compute the probability of observing a
sequence, given a model.
›
Find the state sequence that maximizes the
probability of the given sequence, when the
model is known. (
Viterbi
)
›
Induce the HMM that maximizes the
probability of the given sequence. (Baum

Welch)
K

Means clustering
HMM

based K

means clustering
›
A set of vehicle trajectories (sequences)
›
A set of initial HMM (k HMMs)
Step1: Calculating all the probabilities
Step2: Associating the trajectory with HMM
that maximizes probability of the
trajectory
Step3: Updating HMMs based on the
temporary clustering result
Step4: Repeating step 1, 2 and 3 until
convergence has been reached
Training and testing the model
›
Several instances of conflicting trajectory
pairs train the model to identify mutual
conflicting trajectory clusters
›
New trajectories are associated with certain
trajectory cluster based on the specific HMM
probability maximization
›
Conflicting trajectories are identified by their
clustering result.
C.
Piciarelli
*, G.L.
Foresti
Department of Mathematics and Computer
Science, University of Udine, Via
delle
Scienze
206, 33100 Udine, Italy
Available online 21 April 2006
Problem: Classical two

step clustering algorithm can
not update cluster dynamically
Solution: On

line trajectory clustering approach with a
tree

like structure
Goal:Suit for video surveillances sysytems from image
analysis to behavior analysis to detect anomalous
events
Traditional trajectory clustering not suited for detect
anomalous events
off

line: not useful in activity analysis
video system: complex structure,from moving
ojects(low level) to behaviour analysis (high)
Representing trajectories as a tree of cluster
Trajectory(Ti): represented by a list of vectors
Tij(representing a spatial position at time j)
Clusters(Ci): organized in a tree

like structure that,
augmented with probability information,
represented as a list of vectors
Define a distance or similarit to check if a Ti
matches a given Ci( dynamically), when a Ti
matches a Ci, cluster needs to be updated.
Tree creation steps:
1)building,create tree of clusters from acquried data
dynamically,without waitting the end of trajectory.
2)maintenance as below:
For behaviour analysis, we define that an anomaly is
an event happening rarely. Also we assume that
dangerous events are generally anomalous. An
anomalous trjectory can be defined as a trajectory
matches a path in the tree with low probability.
With probabilitic information, we can implement
anomaly detection.
Papers
Learning
Fashion
Path
modeling
Activity
Analysis
P1
Offline
unsupervised
clustering and 4

D
descriptor
7
abnormal
events
detection
P2
Offline
Semi

supervised
HMM

Based
clustering
Conflicting
traffic
P3
Online
Tree

structure
anomaly
detection
Availability of labelled data for
training/validation is not easy and
unsupervised clustering is
favored
online clustering is very important since
normal behaviour keeps evolving
Approaches robust to noisy
trajectories from tracking is preferred
Questions?
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