Trespassing the Boundaries: Labeling Temporal Bounds for Object Interactions in Egocentric Video
This addresses annotation inconsistencies in egocentric video analysis, which is crucial for researchers and practitioners in computer vision, though it is incremental as it builds on existing cognitive models.
The study identified inconsistencies in temporal boundary annotations for object interactions in egocentric video datasets, showing that state-of-the-art recognition methods suffer up to a 10% performance drop due to these issues. It proposed Rubicon Boundaries for annotation, achieving a 4% overall accuracy increase and improved accuracy for 55% of classes on a public dataset.
Manual annotations of temporal bounds for object interactions (i.e. start and end times) are typical training input to recognition, localization and detection algorithms. For three publicly available egocentric datasets, we uncover inconsistencies in ground truth temporal bounds within and across annotators and datasets. We systematically assess the robustness of state-of-the-art approaches to changes in labeled temporal bounds, for object interaction recognition. As boundaries are trespassed, a drop of up to 10% is observed for both Improved Dense Trajectories and Two-Stream Convolutional Neural Network. We demonstrate that such disagreement stems from a limited understanding of the distinct phases of an action, and propose annotating based on the Rubicon Boundaries, inspired by a similarly named cognitive model, for consistent temporal bounds of object interactions. Evaluated on a public dataset, we report a 4% increase in overall accuracy, and an increase in accuracy for 55% of classes when Rubicon Boundaries are used for temporal annotations.