eventseg

Eventseg is a term used in data analysis and computer vision to refer to the segmentation of a continuous stream, such as video or sensor data, into discrete events. In practice, it denotes methods, datasets, and tooling aimed at identifying where one event ends and the next begins, and often at classifying or labeling the events themselves. The term encompasses a range of approaches rather than a single standardized algorithm.

Cognitive science also features a related concept known as Event Segmentation Theory, which posits that humans naturally parse ongoing activity into meaningful events at points where predictions about future states are challenged. Computational event segmentation seeks to emulate this process, using signals such as changes in visual appearance, motion, audio, object interactions, or narrative cues to infer event boundaries.

Common methods in eventseg include online and offline boundary detection, Bayesian change point models, hidden Markov models, and neural sequence models such as recurrent networks or transformers. Features from multiple modalities—visual, auditory, and contextual cues—are often integrated to improve boundary accuracy. Some approaches first propose candidate events and then refine segment boundaries through optimization or learning-based ranking.

Applications of eventseg span video understanding and analysis, activity recognition, video summarization, surveillance, sports analytics, and human–robot interaction. Datasets frequently used in research include annotated video collections that provide ground-truth event boundaries, while evaluation typically considers boundary detection accuracy, F1 scores, and sequence edit distances. See also event segmentation theory, action recognition, and video parsing for related concepts.