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Towards Coastal Threat Evaluation Decision Support. Presentation by Jacques du Toit Operational Research University of Stellenbosch 3 December 2010. Overview. The Problem Machine Learning/Pattern Recognition Classification Clustering Learning Behavioural Patterns Application Data
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Towards Coastal Threat Evaluation Decision Support Presentation by Jacques du Toit Operational Research University of Stellenbosch 3 December 2010
2/28 Overview The Problem Machine Learning/Pattern Recognition Classification Clustering Learning Behavioural Patterns Application Data Methods Summary
3/28 Background: The Problem Maritime Threats Smuggling Trafficking Poaching/Illegal Fishing Threat Evaluation Detection Prediction Why? Limited resources Vast area
4/28 Background: EEZ Exclusive Economic Zone
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]
6/28 MLPR: Introduction Standard classifier Feature Selection Feature Extraction
7/28 MLPR: Introduction Feature extraction: PCA
8/28 MLPR: Classification Iris Data
9/28 MLPR: Regression Chirps
10/28 MLPR: Learning Training a classifier But does such a system 'learn'?
11/28 MLPR: Supervised/Unsupervised Supervised: Classifier trained on labelled examples Predict class of unseen instance Unsupervised No labels System must 'discover' structure
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
13/28 LBP: Data Considerations Spatio-temporal analysis Noise
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
15/28 LBP: Towards Coastal TE
16/28 Data AIS Data Position Time Speed Course
17/28 Data Area Considered
18/28 Data Update frequency
19/28 Data • Observations per class
20/28 Data Fundamental Assumption
21/28 Preprocessing Approximate Spatial data Least Squares B-Spline curves Resampling Linear method Duplicate times
22/28 Data The behaviour of anchored vessels
23/28 Features Flow vectors Sinuosity and curvature Bounding box Coefficients (parametric methods)
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
25/28 SOM Neural network Unsupervised learning method Online method Johnson & Hogg [3] Construct pdf of point vectors Vector quantization Owens & Hunter [4] Pre-process data
26/28 Summary MLPR Exploratory analysis Real-time Performance evaluation – real data High level language
27/28 Questions
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. [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.
