ESAR, An Expert Shoplifting Activity Recognition System
Shoplifting is a troubling and pervasive aspect of consumers, causing great losses to retailers. It is the theft of goods from the stores/shops, usually by hiding the store item either in the pocket or in carrier bag and leaving without any payment. Revenue loss is the most direct financial effect o...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2022-03-01
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| Series: | Cybernetics and Information Technologies |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/cait-2022-0012 |
| _version_ | 1818055165784096768 |
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| author | Ansari Mohd. Aquib Singh Dushyant Kumar |
| author_facet | Ansari Mohd. Aquib Singh Dushyant Kumar |
| author_sort | Ansari Mohd. Aquib |
| collection | DOAJ |
| description | Shoplifting is a troubling and pervasive aspect of consumers, causing great losses to retailers. It is the theft of goods from the stores/shops, usually by hiding the store item either in the pocket or in carrier bag and leaving without any payment. Revenue loss is the most direct financial effect of shoplifting. Therefore, this article introduces an Expert Shoplifting Activity Recognition (ESAR) system to reduce shoplifting incidents in stores/shops. The system being proposed seamlessly examines each frame in video footage and alerts security personnel when shoplifting occurs. It uses dual-stream convolutional neural network to extract appearance and salient motion features in the video sequences. Here, optical flow and gradient components are used to extract salient motion features related to shoplifting movement in the video sequence. Long Short Term Memory (LSTM) based deep learner is modeled to learn the extracted features in the time domain for distinguishing person actions (i.e., normal and shoplifting). Analyzing the model behavior for diverse modeling environments is an added contribution of this paper. A synthesized shoplifting dataset is used here for experimentations. The experimental outcomes show that the proposed approach attains better consequences up to 90.26% detection accuracy compared to the other prevalent approaches. |
| first_indexed | 2024-12-10T12:08:37Z |
| format | Article |
| id | doaj.art-d3ff59f54cc14d70bc8e9e526a13c648 |
| institution | Directory Open Access Journal |
| issn | 1314-4081 |
| language | English |
| last_indexed | 2024-12-10T12:08:37Z |
| publishDate | 2022-03-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Cybernetics and Information Technologies |
| spelling | doaj.art-d3ff59f54cc14d70bc8e9e526a13c6482022-12-22T01:49:24ZengSciendoCybernetics and Information Technologies1314-40812022-03-0122119020010.2478/cait-2022-0012ESAR, An Expert Shoplifting Activity Recognition SystemAnsari Mohd. Aquib0Singh Dushyant Kumar1CSED, MNNITAllahabad, Prayagraj, IndiaCSED, MNNITAllahabad, Prayagraj, IndiaShoplifting is a troubling and pervasive aspect of consumers, causing great losses to retailers. It is the theft of goods from the stores/shops, usually by hiding the store item either in the pocket or in carrier bag and leaving without any payment. Revenue loss is the most direct financial effect of shoplifting. Therefore, this article introduces an Expert Shoplifting Activity Recognition (ESAR) system to reduce shoplifting incidents in stores/shops. The system being proposed seamlessly examines each frame in video footage and alerts security personnel when shoplifting occurs. It uses dual-stream convolutional neural network to extract appearance and salient motion features in the video sequences. Here, optical flow and gradient components are used to extract salient motion features related to shoplifting movement in the video sequence. Long Short Term Memory (LSTM) based deep learner is modeled to learn the extracted features in the time domain for distinguishing person actions (i.e., normal and shoplifting). Analyzing the model behavior for diverse modeling environments is an added contribution of this paper. A synthesized shoplifting dataset is used here for experimentations. The experimental outcomes show that the proposed approach attains better consequences up to 90.26% detection accuracy compared to the other prevalent approaches.https://doi.org/10.2478/cait-2022-0012automated surveillance systemhuman activity recognition (har)histogram of oriented gradient (hog)optical flowconvolutional neural network (cnn)long short term memory (lstm) |
| spellingShingle | Ansari Mohd. Aquib Singh Dushyant Kumar ESAR, An Expert Shoplifting Activity Recognition System Cybernetics and Information Technologies automated surveillance system human activity recognition (har) histogram of oriented gradient (hog) optical flow convolutional neural network (cnn) long short term memory (lstm) |
| title | ESAR, An Expert Shoplifting Activity Recognition System |
| title_full | ESAR, An Expert Shoplifting Activity Recognition System |
| title_fullStr | ESAR, An Expert Shoplifting Activity Recognition System |
| title_full_unstemmed | ESAR, An Expert Shoplifting Activity Recognition System |
| title_short | ESAR, An Expert Shoplifting Activity Recognition System |
| title_sort | esar an expert shoplifting activity recognition system |
| topic | automated surveillance system human activity recognition (har) histogram of oriented gradient (hog) optical flow convolutional neural network (cnn) long short term memory (lstm) |
| url | https://doi.org/10.2478/cait-2022-0012 |
| work_keys_str_mv | AT ansarimohdaquib esaranexpertshopliftingactivityrecognitionsystem AT singhdushyantkumar esaranexpertshopliftingactivityrecognitionsystem |