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?