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Title: Detecting anomalies from liquid transfer videos in automated laboratory setting
In this work, we address the problem of detecting anomalies in a certain laboratory automation setting. At first, we collect video images of liquid transfer in automated laboratory experiments. We mimic the real-world challenges of developing an anomaly detection model by considering two points. First, the size of the collected dataset is set to be relatively small compared to large-scale video datasets. Second, the dataset has a class imbalance problem where the majority of the collected videos are from abnormal events. Consequently, the existing learning-based video anomaly detection methods do not perform well. To this end, we develop a practical human-engineered feature extraction method to detect anomalies from the liquid transfer video images. Our simple yet effective method outperforms state-of-the-art anomaly detection methods with a notable margin. In particular, the proposed method provides 19% and 76% average improvement in AUC and Equal Error Rate, respectively. Our method also quantifies the anomalies and provides significant benefits for deployment in the real-world experimental setting.  more » « less
Award ID(s):
1949629 2007595 2211597 2205148
PAR ID:
10430541
Author(s) / Creator(s):
; ; ; ; ; ; ;
Date Published:
Journal Name:
Frontiers in Molecular Biosciences
Volume:
10
ISSN:
2296-889X
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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