<p>Most
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 food handling, there exist steps to achieve
good manufacturing practices (GMPs). Traditionally, the assessment of food
handling quality would require hiring a food expert for audit, which is
expensive in cost. Recently, recognizing activities in videos becomes a rapidly
growing field with wide-ranging applications. In this presentation, we propose
to approach the assessment of hand-hygiene quality, which is a crucial step in
food handling, with video analytic methods: action recognition and action
detection algorithms. Our approaches focus on hand-hygiene activities with
different requirements include camera views and scenario variations. </p>
<p> </p>
For hand-hygiene with egocentric video data, we create a two-stage
system to localize and recognize all the hand-hygiene actions in each untrimmed
video. This involves applying a low-cost hand mask and motion histogram
features to localize the temporal regions of hand-hygiene actions. For
hand-hygiene with multi-camera view video data, we design a system processes
untrimmed video from both egocentric and third-person cameras, and each
hand-hygiene action is recognized with its “expert” camera view. For
hand-hygiene across different scenarios, we propose a multi-modality framework
to recognize hand-hygiene actions in untrimmed video sequences. We use
modalities such as RGB, optical flow, hand segmentation mask, and human
skeleton joint modalities to construct individual CNN and apply a hierarchical
method to recognize hand-hygiene action
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/14493822 |
| Date | 27 April 2021 |
| Creators | Chengzhang Zhong (10706937) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Video_processing_for_safe_food_handling/14493822 |
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