<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</abstract>

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.

ESAR, An Expert Shoplifting Activity Recognition System

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"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...

ESAR, An Expert Shoplifting Activity Recognition System

Pubblicato online: 10 apr 2022

Pagine: 190 - 200

Ricevuto: 12 ott 2021

Accettato: 21 gen 2022

DOI: https://doi.org/10.2478/cait-2022-0012

Parole chiave
Automated surveillance system, Human Activity Recognition (HAR), Histogram of Oriented Gradient (HOG), Optical Flow, Convolutional Neural Network (CNN), Long Short Term Memory (LSTM)

© 2022 Mohd. Aquib Ansari et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

ESAR, An Expert Shoplifting Activity Recognition System

Pubblicato online: 10 apr 2022

Pagine: 190 - 200

Ricevuto: 12 ott 2021

Accettato: 21 gen 2022

DOI: https://doi.org/10.2478/cait-2022-0012

Parole chiaveAutomated surveillance system, Human Activity Recognition (HAR), Histogram of Oriented Gradient (HOG), Optical Flow, Convolutional Neural Network (CNN), Long Short Term Memory (LSTM)

© 2022 Mohd. Aquib Ansari et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
Automated surveillance system, Human Activity Recognition (HAR), Histogram of Oriented Gradient (HOG), Optical Flow, Convolutional Neural Network (CNN), Long Short Term Memory (LSTM)