Add a reusable video alignment module for offline multiview workflows. The helper scans per-frame timestamps, builds nearest-timestamp bundle matches under a configurable skew threshold, and rewrites synchronized per-camera videos for downstream detection and tracking runs.
The detection package now exports the alignment primitives, and a test-support CLI is included so dataset-specific experiments can generate aligned clips without expanding the public application surface.
Regression tests cover both bundle matching and frame selection during rewritten video generation.
Refactor the package into common, schema, detection, and tracking namespaces and move dataset-specific ActualTest utilities into tests/support.
Add a pluggable detection stack with typed protocols, pydantic-settings config, loguru-based runner logging, cvmmap and headless video sources, NATS and parquet sinks, and a structured coco-wholebody133 payload path.
Teach tracking replay loading to consume parquet detection directories directly, preserve empty frames, and keep the video-to-parquet-to-tracking workflow usable for offline E2E runs.
Vendor the local mmcv and xtcocotools wheels under Git LFS, update uv sources/lock state, and refresh the mmcv build so mmcv.ops loads successfully with the current torch+cu130 environment.
crosstyan