The document discusses different approaches to abnormal behavior recognition: supervised, unsupervised, and semi-supervised. The supervised approach relies on clearly labeled normal and abnormal data but abnormal behavior is rare and undefined. Unsupervised approaches either cluster patterns or build a database of normal behavior patterns to detect abnormalities. Semi-supervised uses labeled normal data to build a normal model and then learns the abnormal model unsupervised. All approaches still have issues like insufficient labeled data or inconsistent manual labeling.

1. Abnormal Behaviour Recognition
Mahfuzul Haque
www.monash.edu.au

2. Literature
• Supervised Approach
• Unsupervised Approach
• Semi-supervised Approach
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3. Supervised Approach
• Based on the assumption that there exist
well-defined and known a priori
behaviour classes (both normal and
abnormal)
• However, in reality, abnormal behaviour
is both rare and far from being well
defined, resulting in insufficient clearly
labelled data required for supervised
model building.
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4. Unsupervised Approach
• Further categorized into two different
types:
– With explicit behaviour model
– With no behaviour explicit model
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5. Unsupervised – No Explicit Model
• Either clustering on observed patterns and
labelling those forming small clusters as being
abnormal
• Or building a database of spatiotemporal
patches using only regular / normal behaviour
and detecting those patterns that cannot be
composed from the database as being
abnormal.
• Applicability on previously unseen (normal)
behaviour?
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6. Semi-supervised Approach
• Two-stage training process
• Stage one: normal behaviour model is
learned using labelled normal patterns.
• Stage two: an abnormal behaviour model
is then learned unsupervised using
Bayesian adaptation.
• Still suffers from the laborious and
inconsistent manual data labelling
process
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7. Abnormal Behaviour Detection in Literature
Abnormal Behaviour Detection
Supervised
Unsupervised
Manual Labelling,
Prior assumption of well
define behaviour classes
No Behaviour Model
Clustering of observed patterns,
Database of spatiotemporal patches
Semi-supervised
Normal behaviour model using
manual labelling,
Abnormal behaviour model
unsupervised using Bayesian
adaptation
Explicit Behaviour Model
More Recent Approach
Mixture of Dynamic Bayesian Networks (DBNs)
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