Error Detection in Egocentric Procedural Task Videos

We present a new egocentric procedural error dataset containing videos with various types of errors as well as normal videos and propose a new framework for procedural error detection using error-free training videos only. Our framework consists of an action segmentation model and a contrastive step prototype learning module to segment actions and learn useful features for error detection. Based on the observation that interactions between hands and objects often inform action and error understanding, we propose to combine holistic frame features with relations features, which we learn by building a graph using active object detection followed by a Graph Convolutional Network. To handle errors, unseen during training, we use our contrastive step prototype learning to learn multiple prototypes for each step, capturing variations of error-free step executions. At inference time, we use feature-prototype similarities for error detection. By experiments on three datasets, we show that our proposed framework outperforms state-of-the-art video anomaly detection methods for error detection and provides smooth action and error predictions.