Probabilistic Behaviour Signatures: Feature-based behaviour recognition in data-scarce domains

Rolf Baxter, Neil M. Robertson, David Lane

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)
53 Downloads (Pure)


In this paper we present a new method to provide situation awareness via the automatic recognition of behaviour in video. In contrast to many other approaches, the presented method does not require many training exemplars. We introduce Probabilistic Behaviour Signatures to represent the goals of a person agent as sets of features. We do not assume temporal ordering of observed actions is necessary. Inference is performed using an extension of the Rao-Blackwellised Particle Filter. We validate our approach using simulated image trajectories which represent three high-level behaviours. We compare performance to a trained Hidden Markov Model Particle Filter (HMM PF) and show that our approach achieves 92% accuracy at video frame rate. Our method is also significantly more robust than the HMM PF in the presence of noise.

Original languageEnglish
Title of host publication13th Conference on Information Fusion, Fusion 2010
Number of pages8
Publication statusPublished - 2010
Event13th Conference on Information Fusion - Edinburgh, United Kingdom
Duration: 26 Jul 201029 Jul 2010


Conference13th Conference on Information Fusion
Abbreviated titleFusion 2010
Country/TerritoryUnited Kingdom


  • Bayesian inference
  • Behaviour analysis
  • Security
  • Visual surveillance


Dive into the research topics of 'Probabilistic Behaviour Signatures: Feature-based behaviour recognition in data-scarce domains'. Together they form a unique fingerprint.

Cite this