A computer-vision research project targeting real-time perception of dynamic and static scene entities with a path toward wearable integration.
Vie investigates real-time machine perception systems that jointly model moving and static objects, with reproducible data engineering and demo-first iteration.
Vie is centered on biologically inspired scene analysis: learning dynamic interactions in complex environments while maintaining real-time system behavior suitable for downstream deployment contexts.
Vie is early-stage: biologically plausible video scene analysis that learns from real-world dynamics.
Vie targets real-time machine perception that models moving and static objects jointly, with an eye toward wearable-friendly deployment. The near-term focus is the unglamorous groundwork that decides whether the later models mean anything: a reproducible data and annotation pipeline and a clear set of benchmark baselines.
Vie follows the same team discipline as its sibling projects — issue-per-task with an owner, a branch and linked pull request per issue, and CI plus review before merge — with datasets and experiments documented as first-class artifacts.
This is the dataset-and-baseline stage. The charter and roadmap are in place and the first perception pipeline is being built; there is no finished architecture to diagram yet, and the page says so rather than inventing one.