Using Social Signals to Predict Shoplifting: A Transparent Approach to a Sensitive Activity Analysis Problem
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 stat...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-10-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/20/6812 |
| _version_ | 1797513205350662144 |
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| author | Shane Reid Sonya Coleman Philip Vance Dermot Kerr Siobhan O’Neill |
| author_facet | Shane Reid Sonya Coleman Philip Vance Dermot Kerr Siobhan O’Neill |
| author_sort | Shane Reid |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T06:13:23Z |
| format | Article |
| id | doaj.art-76dd1d02b2b04e14ab070d899624fc1d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T06:13:23Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-76dd1d02b2b04e14ab070d899624fc1d2023-11-22T19:57:51ZengMDPI AGSensors1424-82202021-10-012120681210.3390/s21206812Using Social Signals to Predict Shoplifting: A Transparent Approach to a Sensitive Activity Analysis ProblemShane Reid0Sonya Coleman1Philip Vance2Dermot Kerr3Siobhan O’Neill4School of Computing, Engineering and Intelligent Systems, Ulster University, Derry/Londonderry BT48 7JL, UKSchool of Computing, Engineering and Intelligent Systems, Ulster University, Derry/Londonderry BT48 7JL, UKSchool of Computing, Engineering and Intelligent Systems, Ulster University, Derry/Londonderry BT48 7JL, UKSchool of Computing, Engineering and Intelligent Systems, Ulster University, Derry/Londonderry BT48 7JL, UKSchool of Computing, Engineering and Intelligent Systems, Ulster University, Derry/Londonderry BT48 7JL, UKRetail 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.https://www.mdpi.com/1424-8220/21/20/6812human behaviour analysissocial signal processingvideo processingbias detectionethical AImachine learning |
| spellingShingle | Shane Reid Sonya Coleman Philip Vance Dermot Kerr Siobhan O’Neill Using Social Signals to Predict Shoplifting: A Transparent Approach to a Sensitive Activity Analysis Problem Sensors human behaviour analysis social signal processing video processing bias detection ethical AI machine learning |
| title | Using Social Signals to Predict Shoplifting: A Transparent Approach to a Sensitive Activity Analysis Problem |
| title_full | Using Social Signals to Predict Shoplifting: A Transparent Approach to a Sensitive Activity Analysis Problem |
| title_fullStr | Using Social Signals to Predict Shoplifting: A Transparent Approach to a Sensitive Activity Analysis Problem |
| title_full_unstemmed | Using Social Signals to Predict Shoplifting: A Transparent Approach to a Sensitive Activity Analysis Problem |
| title_short | Using Social Signals to Predict Shoplifting: A Transparent Approach to a Sensitive Activity Analysis Problem |
| title_sort | using social signals to predict shoplifting a transparent approach to a sensitive activity analysis problem |
| topic | human behaviour analysis social signal processing video processing bias detection ethical AI machine learning |
| url | https://www.mdpi.com/1424-8220/21/20/6812 |
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