Journal ArticleDetecting anomalies from liquid transfer videos in automated laboratory settingSarker, Najibul Haque; Hakim, Zaber Abdul; Dabouei, Ali; Uddin, Mostofa Rafid; Freyberg, Zachary; MacWilliams, Andy; Kangas, Joshua; Xu, MinIn 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.2023-05-0410430541Frontiers in Molecular Biosciences102296-889Xhttps://doi.org/10.3389/fmolb.2023.11475141949629; 2007595; 2211597; 2205148National Science Foundation