Video-Based Plastic Bag Grabbing Action Recognition: A New Video Dataset and a Comparative Study of Baseline Models
Recognizing the action of plastic bag taking from CCTV video footage represents a highly specialized and niche challenge within the broader domain of action video classification. To address this challenge, our paper introduces a novel benchmark video dataset specifically curated for the task of iden...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/1/255 |
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| author | Pei Jing Low Bo Yan Ng Nur Insyirah Mahzan Jing Tian Cheung-Chi Leung |
| author_facet | Pei Jing Low Bo Yan Ng Nur Insyirah Mahzan Jing Tian Cheung-Chi Leung |
| author_sort | Pei Jing Low |
| collection | DOAJ |
| description | Recognizing the action of plastic bag taking from CCTV video footage represents a highly specialized and niche challenge within the broader domain of action video classification. To address this challenge, our paper introduces a novel benchmark video dataset specifically curated for the task of identifying the action of grabbing a plastic bag. Additionally, we propose and evaluate three distinct baseline approaches. The first approach employs a combination of handcrafted feature extraction techniques and a sequential classification model to analyze motion and object-related features. The second approach leverages a multiple-frame <i>convolutional neural network</i> (CNN) to exploit temporal and spatial patterns in the video data. The third approach explores a 3D CNN-based deep learning model, which is capable of processing video data as volumetric inputs. To assess the performance of these methods, we conduct a comprehensive comparative study, demonstrating the strengths and limitations of each approach within this specialized domain. |
| format | Article |
| id | doaj-art-9a93a8313e9b466486541198937dd4ab |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-9a93a8313e9b466486541198937dd4ab2025-01-10T13:21:22ZengMDPI AGSensors1424-82202025-01-0125125510.3390/s25010255Video-Based Plastic Bag Grabbing Action Recognition: A New Video Dataset and a Comparative Study of Baseline ModelsPei Jing Low0Bo Yan Ng1Nur Insyirah Mahzan2Jing Tian3Cheung-Chi Leung4NUS-ISS, National University of Singapore, Singapore 119615, SingaporeNUS-ISS, National University of Singapore, Singapore 119615, SingaporeNUS-ISS, National University of Singapore, Singapore 119615, SingaporeNUS-ISS, National University of Singapore, Singapore 119615, SingaporeNUS-ISS, National University of Singapore, Singapore 119615, SingaporeRecognizing the action of plastic bag taking from CCTV video footage represents a highly specialized and niche challenge within the broader domain of action video classification. To address this challenge, our paper introduces a novel benchmark video dataset specifically curated for the task of identifying the action of grabbing a plastic bag. Additionally, we propose and evaluate three distinct baseline approaches. The first approach employs a combination of handcrafted feature extraction techniques and a sequential classification model to analyze motion and object-related features. The second approach leverages a multiple-frame <i>convolutional neural network</i> (CNN) to exploit temporal and spatial patterns in the video data. The third approach explores a 3D CNN-based deep learning model, which is capable of processing video data as volumetric inputs. To assess the performance of these methods, we conduct a comprehensive comparative study, demonstrating the strengths and limitations of each approach within this specialized domain.https://www.mdpi.com/1424-8220/25/1/255plastic bag grabbingself-checkoutaction video recognition |
| spellingShingle | Pei Jing Low Bo Yan Ng Nur Insyirah Mahzan Jing Tian Cheung-Chi Leung Video-Based Plastic Bag Grabbing Action Recognition: A New Video Dataset and a Comparative Study of Baseline Models Sensors plastic bag grabbing self-checkout action video recognition |
| title | Video-Based Plastic Bag Grabbing Action Recognition: A New Video Dataset and a Comparative Study of Baseline Models |
| title_full | Video-Based Plastic Bag Grabbing Action Recognition: A New Video Dataset and a Comparative Study of Baseline Models |
| title_fullStr | Video-Based Plastic Bag Grabbing Action Recognition: A New Video Dataset and a Comparative Study of Baseline Models |
| title_full_unstemmed | Video-Based Plastic Bag Grabbing Action Recognition: A New Video Dataset and a Comparative Study of Baseline Models |
| title_short | Video-Based Plastic Bag Grabbing Action Recognition: A New Video Dataset and a Comparative Study of Baseline Models |
| title_sort | video based plastic bag grabbing action recognition a new video dataset and a comparative study of baseline models |
| topic | plastic bag grabbing self-checkout action video recognition |
| url | https://www.mdpi.com/1424-8220/25/1/255 |
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