ab3dmot

# AB3DMOT repo skill Use this skill when a task involves AB3DMOT, 3D multi-object tracking, KITTI/nuScenes tracking inputs, the AB3DMOT tracker API, AB3DMOT result evaluation, confidence thresholding, or qualitative visualization. AB3DMOT is a CPU-oriented Python baseline that combines 3D object detections, a 3D Kalman filter, ego-motion compensation, and Hungarian/greedy data association. It is organized as a repository script workflow rather than an installed console-entry-point package. ## Start here 1. If the task is about installation, imports, dependencies, or runtime smoke checks, read [references/install-and-dependencies.md](references/install-and-dependencies.md) and run [scripts/ab3dmot_environment_check.py](scripts/ab3dmot_environment_check.py). 2. If the task is about repository layout, public source areas, configs, result roots, or which bundled sub-skill owns a file family, read [references/repository-map.md](references/repository-map.md). 3. If the task fails before you know the owning workflow, read [references/troubleshooting.md](references/troubleshooting.md). 4. For provenance and staleness checks, read [references/repo-provenance.md](references/repo-provenance.md). ## Route by workflow - Use [sub-skills/data-conversion/SKILL.md](sub-skills/data-conversion/SKILL.md) for KITTI/nuScenes data placement, detector-result conversion, AB3DMOT detection row validation, category-specific detection folders, and nuScenes-to-KITTI intermediate layouts. - Use [sub-skills/tracking-pipeline/SKILL.md](sub-skills/tracking-pipeline/SKILL.md) for `main.py` tracking commands, config defaults, output naming, direct `AB3DMOT.track` API use, Box3D/matching/Kalman behavior, and one-frame smoke checks. - Use [sub-skills/evaluation-visualization/SKILL.md](sub-skills/evaluation-visualization/SKILL.md) for KITTI 2D/3D MOT metrics, nuScenes official/quick evaluation, confidence thresholding, result export/submission packaging, and image/video visualization. ## Common task routing | User asks for | Read | | --- | --- | | “Validate this AB3DMOT detection file” | `data-conversion` validator and data formats | | “Convert nuScenes detections for AB3DMOT” | `data-conversion` nuScenes conversion reference | | “Run KITTI PointRCNN tracking” | `tracking-pipeline` tracking workflow | | “Use AB3DMOT.track directly” | `tracking-pipeline` API reference and synthetic smoke script | | “Why did `main.py` use nuScenes defaults?” | `tracking-pipeline` configuration/troubleshooting | | “Evaluate KITTI validation with 0.25/0.5/0.7 3D IoU” | `evaluation-visualization` KITTI evaluation | | “Make KITTI 2D MOT submission files” | `evaluation-visualization` KITTI threshold/submission guidance | | “Convert AB3DMOT nuScenes results to JSON and evaluate” | `evaluation-visualization` nuScenes evaluation | | “Render track videos” | `evaluation-visualization` visualization troubleshooting | ## Minimal runtime expectations AB3DMOT command workflows assume a working AB3DMOT checkout or equivalent project tree with: - Python runtime compatible with the repository and dependencies. - NumPy and SciPy in addition to FilterPy, Numba, Matplotlib, Pillow, OpenCV, PyYAML, EasyDict, and the other `requirements.txt` entries. - The external Xinshuo Python toolbox (`Xinshuo_PyToolbox`) importable via `--toolbox-root` or `PYTHONPATH`; it is not bundled or reliably pin-able as a PyPI dependency. - Full external KITTI or nuScenes tracking data when running dataset-level tracking or metrics; detection text files alone are not sufficient for `main.py`. - Optional nuScenes dependencies when running nuScenes conversion or official evaluation. Safe first check from this generated skill directory, pointing it at an AB3DMOT checkout: ```bash python scripts/ab3dmot_environment_check.py --repo-root /path/to/AB3DMOT --smoke-track ``` If the Xinshuo toolbox is not already importable, also pass `--toolbox-root <path-to-Xinshuo_PyToolbox>`. ## Important constraints - AB3DMOT is not a detector; it consumes already-generated 3D detections. - The README quick KITTI demo command is explicit, but `main.py` parser defaults point at nuScenes. Always pass `--dataset`, `--split`, and `--det_name` deliberately. - KITTI `val` maps to the external KITTI `training` tree and a validation sequence list. KITTI `test` maps to the external `testing` tree. - nuScenes tracking uses the repo's KITTI-like `data/nuScenes/nuKITTI/` intermediate tree. - Local test-set labels are unavailable for KITTI and nuScenes; official test metrics require external benchmark servers. ## Verification status for this generated skill This skill's lightweight repair checks cover Python syntax and CLI help only; the synthetic tracker smoke remains dependent on an external AB3DMOT checkout, NumPy/SciPy and the Xinshuo toolbox. Full benchmark-scale tracking/evaluation requires external datasets and is intentionally not run here.