Visual Social Signals for Shoplifting Prediction

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Retail shoplifting is one of the most prevalent forms of theft, estimated to cost UK retailers over £1 billion in 2018. One security measure used to discourage shoplifting is surveillance cameras. However, evidence shows that unless these cameras are constantly monitored, they are ineffective. Automated approaches for detecting suspicious behaviour have proven effective but lack the transparency to enable them to be used ethically in real life scenarios. One way to overcome these problems is through the use of social signals. These are observable behaviours which can be used to predict an individual’s future behaviour. To this end we have developed a set of 15 visual attributes which can be used for shoplifting prediction. We then demonstrate the effectiveness of these attributes by deriving a new dataset of visual social signals attributes by manually annotating videos from the University of central Florida Crimes dataset.
Original languageEnglish
Title of host publicationPATTERNS 2021
Subtitle of host publicationPATTERNS 2021, The Thirteenth International Conference on Pervasive Patterns and Applications
Number of pages5
Publication statusPublished (in print/issue) - 18 Apr 2021
EventNexComm 2021 Congress - Online, Porto, Portugal
Duration: 18 Apr 202122 Apr 2021


ConferenceNexComm 2021 Congress
Internet address


  • Social Signal Processing
  • Shoplifting
  • Activity recognition
  • Video Surveillance
  • Artifical intelligence
  • video analysis
  • SVM
  • XGBoost
  • data analytics


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