anomaly detection - traffic video surveillance
DESCRIPTION
Anomaly Detection - Traffic Video Surveillance. Ziming Zhang, Yucheng Zhao and Yiwen Wan. Outline. Introduction &Motivation Problem Statement Paper Summeries Discussion and Conclusions. What are Anomalies?. - PowerPoint PPT PresentationTRANSCRIPT
Anomaly Detection -Traffic Video Surveillance
Ziming Zhang, Yucheng Zhao and Yiwen Wan
Outline Introduction&Motivation Problem Statement Paper Summeries Discussion and Conclusions
What are Anomalies? Anomaly is a pattern in the data that
does not conform to the expected behaviour
Also referred to as outliers, exceptions, peculiarities, surprise, etc.
Anomaly Detection -Video Traffic Surveillance
outlier
outlier
Vehicle behavior is represented as trajectories
When trajectory does conform to dominant pattern it is detected as anomaly or outlier
Collective Anomalies
Problem Description & Definition
Data Input: Spatio-temperal trajectories of moving objects
Problem Description & Definition
Scene Modeling: • Scene Representation: interest points/path• Learning Model:unsupervised/supervised
Problem Description & Definition
Activity Analysis: virtual fencing, speed profiling, path classification, anomaly detection, online activity analysis and object interaction characterization
Key Challenges 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
3 paper SummariesP1:Event Detection Using Trajectory Clustering and 4-D Histogram
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
Framework
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)
Framework
Representation
Input Trajectories
Initial Clustering
Cluster Representation
using 4-D histogram
Event Detection
Framework
Representation
Input Trajectories
Initial Clustering
Cluster Representation
using 4-D histogram
Event Detection
Framework
Representation
Input Trajectories
Initial Clustering
Cluster Representation
using 4-D histogram
Event Detection
Summary 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
3 paper SummariesP2: Clustering Vehicle Trajectories with Hidden Markov Models Application to Automated Traffic Safety Analysis
Nicolas Saunier and Tarek Sayed
Introduction 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, vx, vy}
Model HMM (Hidden Markov Model)
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)
Model K-Means clustering
Model 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
Model 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.
3 paper SummariesP3: On-line trajectory clustering for anomalous events detection
C. Piciarelli *, G.L. ForestiDepartment of Mathematics and Computer Science, University of Udine, Via delle Scienze 206, 33100 Udine, ItalyAvailable online 21 April 2006
Introduction
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
Problem Definition
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)
Proposed Algorithm 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-like Structure
Tree creation steps: 1)building,create tree of clusters from acquried
data dynamically,without waitting the end of trajectory.
2)maintenance as below:
Summary 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.
Comparison
Papers LearningFashion
Path modeling
Activity Analysis
P1 Offline unsupervised clustering and 4-D descriptor
7 abnormal events
detectionP2 Offline Semi-supervised
HMM-Based clustering
Conflicting traffic
P3 Online Tree-structure anomaly detection
Conclusion 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
Thanks!!! Questions?