---

name: tracking-pipeline description: "Run AB3DMOT tracking safely and use the core AB3DMOT tracker APIs for KITTI and nuScenes 3D MOT workflows." metadata: disco-role: operating disable-model-invocation: true license: NOASSERTION

tracking-pipeline

Use this sub-skill when a task is about running AB3DMOT tracking, constructing a safe main.py command, understanding tracker configuration, or calling the AB3DMOT.track API directly.

Read first

• For command-level tracking workflows, dataset prerequisites, category loops, and output folders, read references/tracking-workflow.md.
• For direct API usage, synthetic smoke behavior, AB3DMOT.track, Box3D, matching, and Kalman filter details, read references/api-reference.md.
• For YAML defaults, CLI override behavior, detector/category compatibility, and config pitfalls, read references/configuration.md.
• For common tracking failures and recovery steps, read references/troubleshooting.md.

Bundled scripts

• scripts/build_tracking_command.py builds explicit, non-running main.py commands and prints the expected input/result folder names without importing AB3DMOT.
• scripts/smoke_track_synthetic.py runs a no-dataset one-frame API smoke. Pass --repo-root /path/to/AB3DMOT and, when Xinshuo is external, --toolbox-root /path/to/Xinshuo_PyToolbox; the helper injects both paths before importing.

Boundaries

This sub-skill owns:

• main.py flags: --dataset, --split, and --det_name.
• Config fields that affect tracking: save_root, dataset, split, det_name, cat_list, score_threshold, num_hypo, ego_com, vis, and affi_pro.
• KITTI and nuScenes detector/category compatibility for tracking.
• Result-directory naming, category-specific tracking, combined results, affinity outputs, and logs.
• Direct use of AB3DMOT, AB3DMOT.track, Box3D, matching, and the Kalman filter state model.

Use sibling skills for neighboring concerns:

• Data download, raw nuScenes conversion, detector-result conversion, and detection schema repair belong to ../data-conversion/SKILL.md.
• Metrics, confidence thresholding, result export, server submission, and visualization belong to ../evaluation-visualization/SKILL.md.

Fast decisions

• For KITTI PointRCNN validation, use explicit flags: python main.py --dataset KITTI --split val --det_name pointrcnn.
• Do not run bare python main.py unless the task intentionally uses the nuScenes config default.
• If the user is unsure which input folders/results will be used, run the bundled command builder first.
• If the user wants to embed AB3DMOT in another Python loop, start with the API reference and synthetic smoke script instead of the dataset-level CLI.
• If full tracking data is missing, do not treat detector text files as enough; route back to data-conversion layout checks.

Minimal checks before running tracking

1. python main.py --help succeeds in the target runtime.
2. The dataset config has the intended split, det_name, cat_list, and save_root.
3. Category-specific detection folders exist for every configured category.
4. The full tracking data root contains calibration, image frame lists, and ego-motion/OXTS data for the split.
5. The result root has enough write space; tracking writes per-frame files, affinity matrices, logs, and combined category outputs.