Using Social Signals to Predict Shoplifting: A Transparent Approach to a Sensitive Activity Analysis Problem

Research output: Contribution to journalArticlepeer-review

6 Downloads (Pure)

Abstract

Abstract: Retail shoplifting is one of the most prevalent forms of theft and has accounted for over
one billion GBP in losses for UK retailers in 2018. An automated approach to detecting behaviours
associated with shoplifting using surveillance footage could help reduce these losses. Until recently,
most state‐of‐the‐art vision‐based approaches to this problem have relied heavily on the use of black
box deep learning models. While these models have been shown to achieve very high accuracy, this
lack of understanding on how decisions are made raises concerns about potential bias in the models.
This limits the ability of retailers to implement these solutions, as several high‐profile legal cases
have recently ruled that evidence taken from these black box methods is inadmissible in court. There
is an urgent need to develop models which can achieve high accuracy while providing the necessary
transparency. One way to alleviate this problem is through the use of social signal processing to add
a layer of understanding in the development of transparent models for this task. To this end, we
present a social signal processing model for the problem of shoplifting prediction which has been
trained and validated using a novel dataset of manually annotated shoplifting videos. The resulting
model provides a high degree of understanding and achieves accuracy comparable with current
state of the art black box methods.
Original languageEnglish
Number of pages19
JournalSensors
Volume21
Issue number20
DOIs
Publication statusPublished - 13 Oct 2021

Keywords

  • human behaviour analysis
  • social signal processing
  • video processing
  • bias detection
  • ethical AI
  • machine learning

Fingerprint

Dive into the research topics of 'Using Social Signals to Predict Shoplifting: A Transparent Approach to a Sensitive Activity Analysis Problem'. Together they form a unique fingerprint.

Cite this