Hand-hygiene activity recognition in egocentric video
Chengzhang Zhong, Amy R. Reibman, Hansel Mina Cordoba, Amanda J. Deering · 2019
Food safety is affected by the conditions and practices during different manufacturing steps to prevent contamination and food-borne illnesses. In this paper, we focus on detecting hand-hygiene actions in Egocentric videos. We create a two-stage system to localize and recognize all the hand-hygiene actions in each untrimmed video. In the first stage, we apply a low-cost hand mask and motion histogram features to localize the temporal regions of hand-hygiene actions. In the second stage, we use the two-stream network model combined with a search algorithm to recognize all types of hand-hygiene actions that happen in the untrimmed video. The system achieves a detection accuracy close to 80% on our dataset with 100 participants.