1. Towards Coastal Threat Evaluation Decision Support Presentation by Jacques du Toit Operational Research University of Stellenbosch 3 December 2010

2. 2/28 Overview The Problem Machine Learning/Pattern Recognition Classification Clustering Learning Behavioural Patterns Application Data Methods Summary

3. 3/28 Background: The Problem Maritime Threats Smuggling Trafficking Poaching/Illegal Fishing Threat Evaluation Detection Prediction Why? Limited resources Vast area

4. 4/28 Background: EEZ Exclusive Economic Zone

5. 5/28 Background: 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]

6. 6/28 MLPR: Introduction Standard classifier Feature Selection Feature Extraction

7. 7/28 MLPR: Introduction Feature extraction: PCA

8. 8/28 MLPR: Classification Iris Data

9. 9/28 MLPR: Regression Chirps

10. 10/28 MLPR: Learning Training a classifier But does such a system 'learn'?

11. 11/28 MLPR: Supervised/Unsupervised Supervised: Classifier trained on labelled examples Predict class of unseen instance Unsupervised No labels System must 'discover' structure

12. 12/28 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

13. 13/28 LBP: Data Considerations Spatio-temporal analysis Noise

14. 14/28 LBP: 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

15. 15/28 LBP: Towards Coastal TE

16. 16/28 Data AIS Data Position Time Speed Course

17. 17/28 Data Area Considered

18. 18/28 Data Update frequency

19. 19/28 Data Observations per class

20. 20/28 Data Fundamental Assumption

21. 21/28 Preprocessing Approximate Spatial data Least Squares B-Spline curves Resampling Linear method Duplicate times

22. 22/28 Data The behaviour of anchored vessels

23. 23/28 Features Flow vectors Sinuosity and curvature Bounding box Coefficients (parametric methods)

24. 24/28 HMM Successfully applied in speech recognition Probabilistic approach Bashir et al [2] Hidden states modelled as GMM's Temporal causality Subtrajectories represented by PCA coefficients

25. 25/28 SOM Neural network Unsupervised learning method Online method Johnson & Hogg [3] Construct pdf of point vectors  Vector quantization Owens & Hunter [4] Pre-process data

26. 26/28 Summary MLPR Exploratory analysis Real-time Performance evaluation – real data High level language

27. 27/28 Questions

28. 28/28 References [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.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.