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Man in the Loop

  • Shaogang Gong
  • Tao Xiang

Abstract

Video data captured from a public space is typically characterised by highly imbalanced behaviour class distribution. Most of the captured data examples reflect normal behaviours. Unusual behaviours, either because of being rare or abnormal, only constituent a small portion in the observed data. Whilst an unsupervised learning based model can be constructed to detect unusual behaviours through a process of outlier detection, an outlier detection based model is fundamentally limited in a number of ways: (1) The model has difficulties in detecting subtle unusual behaviours. (2) The model does not exploit information from detected unusual behaviours. Image noise and errors in feature extraction could be mistaken as genuine unusual behaviours of interest, giving false alarms in detection. (3) A large amount of rare but benign behaviours give rise to false alarms in unusual behaviour detection. Human knowledge can be exploited in a different way to address this problem. Instead of giving supervision on the input to model learning by labelling the training data, human knowledge can be more effectively utilised by giving selective feedback to model output. In this chapter, we describe an active learning model for seeking human feedback on model selected queries. The query selection criteria are internal to the model rather than decided by the human observer. This is to ensure that not only the most relevant human feedback is sought, but also the model is not subject to human bias without data support.

Keywords

Active Learning Outlier Detection Decision Boundary Unusual Behaviour Incremental Learning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.School of Electronic Engineering and Computer ScienceQueen Mary University of LondonLondonUK
  2. 2.School of Electronic Engineering and Computer ScienceQueen Mary University of LondonLondonUK

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