Towards Coastal Threat Towards Coastal Threat Evaluation Decision Evaluation Decision SupportSupport

Presentation by Jacques du Toit

Operational Research

University of Stellenbosch

3 December 2010

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OverviewOverview

The Problem Machine Learning/Pattern Recognition

Classification Clustering

Learning Behavioural Patterns Application

Data Methods

Summary

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Background: The ProblemBackground: The Problem

Maritime Threats Smuggling Trafficking Poaching/Illegal Fishing

Threat Evaluation Detection Prediction

Why? Limited resources Vast area

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Background: EEZBackground: EEZ

Exclusive Economic Zone

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Background: AwarenetBackground: Awarenet

Maritime area surveillance system Sense, detect & track Recognise/identify Assess threat

Complex System Integration of external data

Data Processing Class estimation Behavioural analysis Intent estimation/threat level

[1]

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MLPR: IntroductionMLPR: Introduction

Standard classifier

Feature Selection Feature Extraction

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MLPR: IntroductionMLPR: Introduction

Feature extraction: PCA

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MLPR: ClassificationMLPR: Classification

Iris Data

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MLPR: RegressionMLPR: Regression

Chirps

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MLPR: LearningMLPR: Learning

Training a classifier

But does such a system 'learn'?

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MLPR: Supervised/UnsupervisedMLPR: Supervised/Unsupervised

Supervised: Classifier trained on labelled examples Predict class of unseen instance

Unsupervised No labels System must 'discover' structure

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Learning Behavioural Patterns (LBP)Learning Behavioural Patterns (LBP)

Computer Vision Video surveillance

Event Recognition Detection/classification of highway lanes

Design of virtual spaces Behaviour Analysis

Ecological modelling Pedestrian movement

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LBP: Data ConsiderationsLBP: Data Considerations

Spatio-temporal analysis Noise

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LBP: Towards Coastal TELBP: Towards Coastal TE

Why this approach? Vessels movement not random Persistent sensors Volumes of data

Requirements Online Anomaly/novelty detection Flexible/robust Measure of uncertainty

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LBP: Towards Coastal TELBP: Towards Coastal TE

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DataData

AIS Data Position Time Speed Course

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DataData

Area Considered

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DataData

Update frequency

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DataData

Observations per class

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DataData

Fundamental Assumption

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PreprocessingPreprocessing

Approximate Spatial data Least Squares B-Spline curves

Resampling Linear method

Duplicate times

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DataData

The behaviour of anchored vessels

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FeaturesFeatures

Flow vectors Sinuosity and curvature Bounding box Coefficients (parametric methods)

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HMMHMM

Successfully applied in speech recognition Probabilistic approach Bashir et al [2]

Hidden states modelled as GMM's Temporal causality Subtrajectories represented by PCA coefficients

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SOMSOM

Neural network Unsupervised learning method Online method Johnson & Hogg [3]

Construct pdf of point vectors Vector quantization

Owens & Hunter [4] Pre-process data

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SummarySummary

MLPR Exploratory analysis Real-time Performance evaluation – real data High level language

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QuestionsQuestions

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ReferencesReferences

[1] CSIR, Awarenet: Persistent, ubiquitous surveillance technologies for enhanced national security, [Online], 2007, [Cited June 7th, 2010], Available from www.csir.co.za/dpss/pdf/protect_waters.pdf.

[2] Bashir FI, Khokhar AA & Schonfeld D, 2007, Object trajectory-based activity classification and recognition using hidden markov models, IEEE Transactions on Image Processing, 16(7), pp. 1912–1919.

[3] Johnson N & Hogg D, 1996, Learning the distribution of object trajectories for event recognition, Image and Vision Computing, 14(8), pp. 609–615.

[4] Owens, J. & Hunter, A, 2000, Application of the self-organising map to trajectory classification, Proceedings of third IEEE International Workshop on Visual Surveillance, pp. 77-83.