feat!: reorganize detection and tracking pipeline

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.

README.md

@@ -9,13 +9,13 @@ Offline multiview body tracking experiments built around:
 ## Install
 
 ```bash
-uv sync --extra dev
+uv sync --group dev
 ```
 
 ## Run
 
 ```bash
-uv run pose-tracking-exp run data/scene.json data/replay.jsonl
+uv run pose-tracking-exp run_tracking data/scene.json data/replay.jsonl
 ```
 
 `scene.json` may declare camera extrinsics in either format:
@@ -26,13 +26,58 @@ uv run pose-tracking-exp run data/scene.json data/replay.jsonl
 The loader normalizes both to OpenCV extrinsics for reprojection and converts to RPT pose only when building the triangulation config.
 If you already have an older hand-authored scene file that stored RPT camera pose directly, set `extrinsic_format` explicitly to `rpt_camera_pose`.
 
-## Convert ParaJumping Payload Records
+## Convert cvmmap Pose Payload Records
 
 ```bash
-uv run pose-tracking-exp convert-parajumping input.jsonl output.jsonl
+uv run pose-tracking-exp convert-cvmmap-pose input.jsonl output.jsonl
 ```
 
-## ActualTest Calibration Caveat
+The current cvmmap `.pose` wire format is fixed to `COCO-WholeBody-133` keypoints.
+That is a transport compatibility constraint, not a tracker limitation: the tracker-side normalizer accepts both `coco17` and `coco_wholebody133`, because the first 17 body joints share the standard COCO ordering.
+
+References:
+
+- https://mmpose.readthedocs.io/en/latest/dataset_zoo/2d_wholebody_keypoint.html
+- https://github.com/jin-s13/COCO-WholeBody
+
+## Run Detection
+
+```bash
+uv sync --group dev --group detection
+uv run pose-tracking-exp run_detection --config detection.toml camera0 camera1
+uv run pose-tracking-exp run_detection --source video --output-dir data/detections --config detection.toml cam0=/data/cam0.mp4 cam1=/data/cam1.mp4
+```
+
+The embedded 2D detection module is organized as a swapable shim:
+
+- `FrameSource`: where images come from
+- `PoseShim`: object detection + pose estimation backend
+- `PoseSink`: where structured detections are published or stored
+
+The default backend is `yolo_rtmpose`, and the heavy runtime dependencies live in the optional `detection` dependency group.
+Checkpoint paths are explicit config fields; the code does not hardcode local checkpoint locations.
+The only inferred path is the MMPose config path, which is resolved relative to the installed `mmpose` package when `pose_config_path` is omitted.
+For offline video runs, the default sink is parquet and writes one `*_detected.parquet` file per source. `run_tracking` can consume that directory directly as replay input.
+
+Example `detection.toml`:
+
+```bash
+instances = ["camera0", "camera1"]
+device = "cuda"
+yolo_checkpoint = "/path/to/yolo_checkpoint.pt"
+pose_checkpoint = "/path/to/coco_wholebody_pose_checkpoint.pth"
+```
+
+## Actual Test Helper
+
+```bash
+uv run --group dev --group detection python -m tests.support.actual_test /mnt/hddl/data/ActualTest_WeiHua --segment Segment_2 --frame-start 1100 --max-frames 120
+```
+
+`actual_test` is a test/support helper, not part of the public installed CLI surface.
+It keeps the union of per-camera frame indices and fills missing camera rows with empty detections, so later 2-camera stretches are still usable instead of being dropped by a 4-camera intersection.
+
+## Actual Test Calibration Caveat
 
 `ActualTest_WeiHua/camera_params.parquet` appears to store raw OpenCV extrinsics from the ChArUco pipeline, not camera poses. The tracker now converts those values before calling `RapidPoseTriangulation`, because RPT expects camera centers and camera-to-world rotation.