anomaly detection in surveillance
DESCRIPTION
Research in human gestures recognitionTRANSCRIPT
Learning Abnormal Behavioural
for Surveillance Systems
By Ahmed Ibrahim
23/2/2011
Outline
• Introduction
• Key Challenges
• Proposed approaches
• Preliminary experiments
• Discussion and future directions
What is Abnormal Behaviour ?
• Abnormal Behaviour is a pattern in the data
that does not conform to the expected normal
behaviour.
• Also referred to as outliers, exceptions,
suspicion, surprise, etc.
• Example: set of data points
Feature space.
F1
F2
N1
N2
o1
o2
O3
Video Surveillance Example
A car on pedestrian roadway
A piece of luggage left in a check in-area
Source: Performance Evaluation of Tracking and Surveillance dataset (PETS)
Key Challenges
• Defining a representative normal behaviour is challenging.
• The boundary between normal and outlying behaviour is often not precise.
• The exact notion of an outlier is different for different video surveillance applications.
• Availability of labelled data for training/validation.
• Data always contain noise.
• Normal behaviour keeps evolving
Abnormal Behavior Detection
Framework
Motion Detection
• Background
subtraction
• Temporal
differencing
• Optical flow
Object Tracking
• Model
• Region
• Active contour
• Feature based
Behavior
Understanding
• Classification
(supervised)
• Clustering
(unsupervised)
Behavior Type
• Label
• Score
Source: PETS
Unsupervised Behavior Modeling
•The following trajectory
has been generating by:
•Applying principal
component analysis
on the video stream;
•Selecting the first
three components
•Every point on the
trajectory represents a
frame from the video
stream
Learning Outliers
Behavior
Model
Gathering
Real Data
Statistically
Resampling
Visually
Resampling
Proposed Statistical Approach
Statistical Resampling Example
Subspace
trajectory of
waking pedestrian
in outdoor
PC: for principal components
Irregular segments
Proposed Visual Approach
Visual Resampling Example
Subspace
trajectory of
waking pedestrian
Subspace
trajectory of
synthetic
pedestrian
Time delay
Similar segments PC: for principal components
Behavior Model Output
• Label : each test instance is given a normal or
anomaly label
• Score: each test instance is assigned an
anomaly score
• Allows the output to be ranked
• Requires an additional threshold parameter
Research Plan
• To test the feasibility of statistical resampling: – The Ionosphere dataset from UCI machine learning
Repository will be used.
– This dataset are radar signals sent into the ionosphere and the class value indicates whether or not the signal returned information “Good” or “Bad” on the structure of the ionosphere.
• To test the feasibility of visual resampling: A set of animated videos with real backgrounds will be generated for the following events:– Walk, Run, Jump, Gallop sideways, Bend , One-hand wave,
Two-hands wave, Jump in place, Jumping Jack, Skip.
References
• [1] Weiming Hu; Tieniu Tan; Liang Wang; S. Maybank; , "A survey on visual surveillance of object motion and behaviors,"Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions, 2004
• [2] Monekosso, Remagnino, ”Synthetic Training Data Generation for Activity Monitoring and Behavior Analysis” , Proceedings of the European Conference on Ambient Intelligence, 2009.
• [3] Garg, Aggarwal, Sofat, ” Vision Based Hand Gesture Recognition”, 2009.
• [4] Poppe, “A survey on vision-based human action recognition”, Image and Vision Computing Journal, 2010.
• [5] Mitra, Acharya, ”Gesture Recognition: A Survey”, IEEE transactions on systems, man, and cybernetics, 2007.
• [6] Wang, Suter, “Recognizing Human Activities from Silhouettes: Motion Subspace and Factorial Discriminative Graphical Model”, IEEE Conference on In Computer Vision and Pattern Recognition, 2007.
• [7] Hua, “Probabilistic Variational Methods for Vision based Complex Motion Analysis”, PhD dissertation, Electrical and Computer Engineering, 2006.
• [8] Incertis, Garcia-Bermejo, Casanova, "Hand Gesture Recognition for Deaf People Interfacing", 18th International Conference on Pattern Recognition, 2006.