deepstream-run-mv3dt

Skill: Run DeepStream MV3DT

When to Use This Skill

Activate this skill when the user wants to set up, run, verify, or debug the DeepStream Multi-View 3D Tracking reference app. Typical prompts:

• "set up MV3DT DeepStream"
• "run the 4-camera MV3DT sample"
• "run the 12-camera MV3DT sample"
• "run MV3DT on my synchronized MP4s"
• "I have videos but no calibration; calibrate and run MV3DT"
• "show the BEV visualizer or Kafka metadata"
• "stop MV3DT" / "clean up MV3DT" / "tear down MV3DT"

Do not use this skill for single-view 3D tracking, generic DeepStream app development, or live-stream onboarding unless the user explicitly maps that work to this repo's MV3DT pipeline.

Examples

• "Deploy the DeepStream MV3DT 4-camera sample and show the OSD and BEV windows."
• "Run the MV3DT 12-camera sample headlessly with RTDETR and save videos."
• "Run MV3DT on synchronized MP4s under /data/mv3dt-demo using PeopleNetTransformer."
• "My custom MV3DT videos do not have calibration; use AutoMagicCalib, then run MV3DT."

Overview

Operate the Multi-View 3D Tracking reference app in DeepStream/src/apps/reference_apps/deepstream-tracker-3d-multi-view using the DeepStream Container path. The skill supports setup, shipped sample runs, custom synchronized MP4 datasets, calibration handoff to AutoMagicCalib, display/headless execution, OSD/BEV outputs, and Kafka metadata inspection.

Prerequisites

• MV3DT reference app directory on disk under DeepStream/src/apps/reference_apps/deepstream-tracker-3d-multi-view; if it is absent, ask before cloning the public DeepStream repo
• Docker with NVIDIA GPU support
• DeepStream container image access
• MV3DT sample datasets, models, custom parser libraries, Kafka, Mosquitto, and mv3dt_venv prepared by the repo setup script
• Working X11/VNC display for live OSD/BEV windows, or the saved-output headless path for tiled DeepStream MP4 plus Kafka-derived BEV MP4

Instructions

Step 0: Resolve MV3DT App Checkout

The skill can be installed outside the DeepStream repo. Resolve REPO_ROOT to the MV3DT app directory, not necessarily the Git top-level directory.

MV3DT_APP_SUBDIR="src/apps/reference_apps/deepstream-tracker-3d-multi-view"

If the app directory cannot be resolved, ask the user for an existing checkout path or for approval to clone https://github.com/NVIDIA/DeepStream. Do not clone silently.

Step 1: Select The Primary Workflow

Load exactly one primary reference for the user's current request:

If setup, datasets, models, Kafka, Mosquitto, Docker GPU runtime, or the Python venv are missing, load references/setup.md before continuing to the user's original workflow.

When sample-run.md or custom-dataset.md needs to regenerate DeepStream configs, load references/generate-configs.md as the canonical shared config-generation reference rather than duplicating the shell logic.

Step 2: Follow The Run Stages

For every run, use this stage order:

Step 3: Apply Defaults Explicitly

• Runtime path: DeepStream Container.
• Display path: if a working X11/VNC display is available, use the repo quick-start path with OSD and BEV windows. The OSD window does not save MP4 by default; when the user explicitly asks to save output in display mode, regenerate configs with both --enable-osd and --enable-file-output plus --enable-msg-broker.
• Headless path: if no working display is available, use saved outputs by default. Generate configs with --enable-file-output and --enable-msg-broker; start offline BEV capture before DeepStream so the run produces both the tiled DeepStream MP4 and the Kafka-derived BEV MP4.
• Sample dataset: support both shipped 4-camera and 12-camera datasets. If the user does not specify a sample, run the 4-camera sample first, then mention in the final report that the 12-camera sample is also available and can be run next.
• Detector: PeopleNetTransformer unless the user asks for RTDETR or PeopleNet2.6.3; carry the selected DETECTOR_MODEL through config generation and AMC camInfo modelInfo normalization. For custom calibration handoff, ask the user to choose the AutoMagicCalib detector instead of silently defaulting.
• Custom data source: synchronized MP4 files. Live-stream handling is outside this first-release skill.
• Calibration handoff: use standalone AutoMagicCalib skills instead of duplicating their setup or API workflow.

Step 4: Preserve Idempotency And User Data

• Readiness checks are safe to rerun.
• Setup may install packages, pull containers, download models, and start services; ask first.
• Config generation and DeepStream runs update generated files under EXPERIMENT_DIR; record RUN_STARTED_AT and do not report old artifacts as current-run success.
• Normal teardown stops only current run processes and leaves Kafka, Mosquitto, models, datasets, and generated artifacts in place unless the user explicitly asks to stop services or delete files.
• Custom datasets are user data. Copy missing calibration-format variants by default, and ask before renaming, overwriting, clearing, or deleting dataset files.

Success Criteria

• Prerequisite checks pass or the missing prerequisite is reported with a narrow next step.
• DeepStream run eventually prints App run successful.
• Display mode shows the DeepStream OSD grid and live BEV visualizer by default.
• Headless mode produces a fresh ${EXPERIMENT_DIR}/outVideos/tiled_display_raw.mp4 and attempts BEV MP4 capture by default.
• Kafka topic mv3dt receives current-run protobuf metadata when message broker output is enabled.
• BEV MP4 is reported as successful only when the separate BEV capture process produced nonzero messages and frames.

Key Output

• Generated configs: ${EXPERIMENT_DIR}/config_deepstream.txt, ${EXPERIMENT_DIR}/config_tracker.yml, ${EXPERIMENT_DIR}/config_msgconv.txt
• Saved DeepStream tiled MP4 when file output is enabled: ${EXPERIMENT_DIR}/outVideos/tiled_display_raw.mp4
• Saved BEV MP4 when offline capture is used: ${EXPERIMENT_DIR}/bev_outputs/trajectory_video_<timestamp>.mp4
• Kafka topic: mv3dt
• Sample output roots: ${REPO_ROOT}/experiments/deepstream/4cam and ${REPO_ROOT}/experiments/deepstream/12cam

Troubleshooting

Safety Notes

• Ask before commands that use sudo, install packages, pull containers, download models, start or stop services, change host display access, overwrite dataset files, or clear generated state.
• Treat deleting Kafka, Mosquitto, models, datasets, or experiment outputs as destructive cleanup. Show the exact targets and get explicit confirmation before removing anything.
• Before running docker run --privileged --net=host, explicitly state that the container gets broad host, device, network, and mounted-repo access, then get user approval.
• Do not silently clone repositories, change host permissions outside the repo, rename or delete user datasets, or report old artifacts as current-run success.
• Treat custom videos, calibration, saved visualizations, and tracking metadata as potentially sensitive local data. Keep outputs local unless the user explicitly asks to move or share them.
• If permission fixes are needed for generated outputs, propose the narrowest generated-directory-only fix and ask first; never recommend broad world-writable recursive permission changes.

Related Skills

• amc-setup-calibration-stack - Launch the standalone AutoMagicCalib stack when calibration is needed.
• amc-run-video-calibration - Generate calibration from synchronized local MP4s before returning to MV3DT.