Multi-View Hand-Hygiene Recognition for Food Safety
A majority of foodborne illnesses result from inappropriate food handling practices. One proven practice to reduce pathogens is to perform effective hand-hygiene before all stages of food handling. In this paper, we design a multi-camera system that uses video analytics to recognize hand-hygiene act...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-11-01
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| Series: | Journal of Imaging |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-433X/6/11/120 |
| _version_ | 1797548510726324224 |
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| author | Chengzhang Zhong Amy R. Reibman Hansel A. Mina Amanda J. Deering |
| author_facet | Chengzhang Zhong Amy R. Reibman Hansel A. Mina Amanda J. Deering |
| author_sort | Chengzhang Zhong |
| collection | DOAJ |
| description | A majority of foodborne illnesses result from inappropriate food handling practices. One proven practice to reduce pathogens is to perform effective hand-hygiene before all stages of food handling. In this paper, we design a multi-camera system that uses video analytics to recognize hand-hygiene actions, with the goal of improving hand-hygiene effectiveness. Our proposed two-stage system processes untrimmed video from both egocentric and third-person cameras. In the first stage, a low-cost coarse classifier efficiently localizes the hand-hygiene period; in the second stage, more complex refinement classifiers recognize seven specific actions within the hand-hygiene period. We demonstrate that our two-stage system has significantly lower computational requirements without a loss of recognition accuracy. Specifically, the computationally complex refinement classifiers process less than 68% of the untrimmed videos, and we anticipate further computational gains in videos that contain a larger fraction of non-hygiene actions. Our results demonstrate that a carefully designed video action recognition system can play an important role in improving hand hygiene for food safety. |
| first_indexed | 2024-03-10T15:00:26Z |
| format | Article |
| id | doaj.art-075b9d9b17ea4334857964ce2d7978ea |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-10T15:00:26Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-075b9d9b17ea4334857964ce2d7978ea2023-11-20T20:09:04ZengMDPI AGJournal of Imaging2313-433X2020-11-0161112010.3390/jimaging6110120Multi-View Hand-Hygiene Recognition for Food SafetyChengzhang Zhong0Amy R. Reibman1Hansel A. Mina2Amanda J. Deering3School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USASchool of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USADepartment of Food Science, Purdue University, West Lafayette, IN 47907, USADepartment of Food Science, Purdue University, West Lafayette, IN 47907, USAA majority of foodborne illnesses result from inappropriate food handling practices. One proven practice to reduce pathogens is to perform effective hand-hygiene before all stages of food handling. In this paper, we design a multi-camera system that uses video analytics to recognize hand-hygiene actions, with the goal of improving hand-hygiene effectiveness. Our proposed two-stage system processes untrimmed video from both egocentric and third-person cameras. In the first stage, a low-cost coarse classifier efficiently localizes the hand-hygiene period; in the second stage, more complex refinement classifiers recognize seven specific actions within the hand-hygiene period. We demonstrate that our two-stage system has significantly lower computational requirements without a loss of recognition accuracy. Specifically, the computationally complex refinement classifiers process less than 68% of the untrimmed videos, and we anticipate further computational gains in videos that contain a larger fraction of non-hygiene actions. Our results demonstrate that a carefully designed video action recognition system can play an important role in improving hand hygiene for food safety.https://www.mdpi.com/2313-433X/6/11/120egocentric videoactivity recognitiondeep learningtemporal segmentation |
| spellingShingle | Chengzhang Zhong Amy R. Reibman Hansel A. Mina Amanda J. Deering Multi-View Hand-Hygiene Recognition for Food Safety Journal of Imaging egocentric video activity recognition deep learning temporal segmentation |
| title | Multi-View Hand-Hygiene Recognition for Food Safety |
| title_full | Multi-View Hand-Hygiene Recognition for Food Safety |
| title_fullStr | Multi-View Hand-Hygiene Recognition for Food Safety |
| title_full_unstemmed | Multi-View Hand-Hygiene Recognition for Food Safety |
| title_short | Multi-View Hand-Hygiene Recognition for Food Safety |
| title_sort | multi view hand hygiene recognition for food safety |
| topic | egocentric video activity recognition deep learning temporal segmentation |
| url | https://www.mdpi.com/2313-433X/6/11/120 |
| work_keys_str_mv | AT chengzhangzhong multiviewhandhygienerecognitionforfoodsafety AT amyrreibman multiviewhandhygienerecognitionforfoodsafety AT hanselamina multiviewhandhygienerecognitionforfoodsafety AT amandajdeering multiviewhandhygienerecognitionforfoodsafety |