| --- |
| license: other |
| task_categories: |
| - video-classification |
| - object-detection |
| - image-classification |
| language: |
| - en |
| tags: |
| - casualty |
| - medical |
| - simulation |
| - UGV |
| - triage |
| - vitals |
| - trauma |
| - DARPA |
| --- |
| # DARPA PHASE 1+2+3 |
|
|
| Multi-modal casualty simulation dataset collected from UGV (ground) platforms across multiple field exercises and phases. The repository bundles casualty video, ground/medic camera footage, still imagery, timestamped manikin vitals, and per-casualty trauma annotations. |
|
|
| ## Dataset Structure |
|
|
| ``` |
| ├── DTC_Trauma_Phase1and2.csv Trauma labels for Phase 1 & 2 (keyed by video_id) |
| ├── DTC_Trauma_Sheet_Phase_3.csv Trauma labels for Phase 3 (keyed by casualty clip path) |
| ├── PHASE_1/ |
| │ └── P1_C{n}_{seg}.mp4 Per-casualty video segments (Casualty 1–3, segments a–l) |
| ├── PHASE_2/ |
| │ └── P2D2_G1_S{n}.MP4 Ground platform video sessions |
| └── PHASE_3/ |
| └── UGV/ Ground (unmanned ground vehicle) platform |
| ├── Field 2/ |
| │ ├── Field 2 Manikin Timestamped Vitals/ (Casualty 6–11 vitals CSVs) |
| │ ├── Camera Snapshots/ |
| │ │ ├── UGV1(iphone) Casualty & scene stills (.png) |
| │ │ ├── UGV2(GoPro) Casualty & scene stills |
| │ │ └── UGV3(DSLR) Casualty & scene stills |
| │ ├── UGV2/ |
| │ │ ├── casualty_6 … casualty_11 Per-casualty MP4 clips |
| │ │ └── Medic View/ Body-worn medic camera, per casualty |
| │ └── UGV4/ |
| │ ├── Camcorder1_23229 Per-casualty camcorder MP4s |
| │ └── Camcorder2_23230 Per-casualty camcorder MP4s |
| └── Field 3/ |
| ├── Field 3 Manikin Timestamped Vitals/ (Casualty 12–19 vitals CSVs) |
| ├── Camera Snapshots/ |
| │ └── UGV2(GoPro) Casualty & scene stills |
| ├── UGV2/ |
| │ ├── casualty_12_14 … casualty_19 Per-casualty MP4 clips |
| │ └── Medic View/ Body-worn medic camera, per casualty |
| └── UGV4/ |
| ├── Camcorder1_23229 |
| └── Camcorder2_23230 |
| ``` |
|
|
| ## Annotations |
|
|
| Two top-level CSVs provide per-casualty trauma labels: |
|
|
| | File | Key column | Label columns | |
| |------|-----------|---------------| |
| | `DTC_Trauma_Phase1and2.csv` | `video_id` (e.g. `P1_C1_a`) | `trauma_head`, `trauma_torso`, `trauma_arm`, `trauma_leg`, `severe_hemorrhage`, `motor_alertness` | |
| | `DTC_Trauma_Sheet_Phase_3.csv` | `casualty_id` (relative clip path, e.g. `UGV/Field 2/UGV2/casualty_6/P3D2_GA32_S1.mp4`) | `trauma_head`, `trauma_torso_front`, `trauma_torso_back`, `trauma_arm_right`, `trauma_arm_left`, `trauma_leg_right`, `trauma_leg_left` | |
|
|
| > **Note:** In videos with multiple casualties, the casualty closer to the camera is taken as the query subject. |
|
|
| ### Label legends |
|
|
| **Phase 1 & 2** (`DTC_Trauma_Phase1and2.csv`) — per-region injury code: |
|
|
| | Value | Meaning | |
| |-------|---------| |
| | `0` | Normal / no injury | |
| | `1` | Wound | |
| | `2` | Amputation (arms and legs only) | |
|
|
| `severe_hemorrhage` and `motor_alertness` are binary flags (`0`/`1`). |
|
|
| **Phase 3** (`DTC_Trauma_Sheet_Phase_3.csv`) — per-region injury code: |
|
|
| | Value | Meaning | |
| |-------|---------| |
| | `0` | Amputation | |
| | `1` | Open wound | |
| | `2` | Closed wound | |
| | `3` | Burn | |
| | `4` | Not testable | |
|
|
| ## Platforms |
|
|
| | Platform | Type | Phases / Fields | Sensors / Devices | |
| |----------|--------|-----------------|----------------------------------------------------| |
| | UGV | Ground | Phase 2, Phase 3 (Field 2, 3) | iPhone & DSLR snapshots, GoPro, Camcorders, Medic cam | |
|
|
| ## Naming Convention |
|
|
| - **Phase/Day**: `P1`, `P2D2`, `P3D2`, `P3D3` — Phase, Day |
| - **Casualty**: `C1`–`C3` (Phase 1) / `casualty_6`–`casualty_19` (Phase 3) |
| - **Camera positions**: `CA`, `CB`, `GA`, `GB`, `G1`, `M` (Medic) |
| - **Segment / Session**: `_a`–`_l` (Phase 1 segments), `S1`, `S10` (sessions) |
|
|
| ### Example filenames |
|
|
| - `P1_C1_a.mp4` — Phase 1, Casualty 1, segment a |
| - `P2D2_G1_S10.MP4` — Phase 2 Day 2, ground camera G1, session 10 |
| - `P3D2_GA32_S1.mp4` — Phase 3 Day 2, casualty clip (Field 2, Casualty 6) |
| - `Casualty6_timestamped_vitals_P3D2_CA36_S1.csv` — Vitals for Casualty 6, Phase 3 Day 2 |
| - `Field 2_Cas 10_1.png` — Snapshot of Casualty 10, Field 2 |
|
|
| ## Modalities |
|
|
| 1. **Video (MP4/MOV)** — Phase 1/2 casualty clips, UGV ground teleoperation, and body-worn medic camera |
| 2. **Images (PNG/JPG)** — Stills from iPhone, GoPro, and DSLR capturing casualties and scene context |
| 3. **Vitals (CSV)** — Timestamped manikin physiological data (simulated patient vitals) |
| 4. **Annotations (CSV)** — Per-casualty trauma severity / condition labels |
|
|
| ## Casualties by Field |
|
|
| | Phase / Field | Casualties | Platforms | |
| |---------------|------------|-----------| |
| | Phase 1 | 1–3 | Casualty video | |
| | Phase 2 | — | UGV (ground) | |
| | Phase 3 / Field 2 | 6–11 | UGV | |
| | Phase 3 / Field 3 | 12–19 | UGV | |
|
|
| ## Basic Usage (Hugging Face Hub) |
|
|
| Download the full repository snapshot: |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| |
| local_dir = snapshot_download( |
| repo_id="vedkdev/DARPA-PHASE-3", |
| repo_type="dataset", |
| ) |
| print(local_dir) |
| ``` |
|
|
| Grab a single file (e.g. the Phase 3 label sheet) without cloning everything: |
|
|
| ```python |
| from huggingface_hub import hf_hub_download |
| |
| csv_path = hf_hub_download( |
| repo_id="vedkdev/DARPA-PHASE-3", |
| repo_type="dataset", |
| filename="DTC_Trauma_Sheet_Phase_3.csv", |
| ) |
| ``` |
|
|
| Load the trauma annotations and resolve clip paths: |
|
|
| ```python |
| import os |
| import pandas as pd |
| |
| labels = pd.read_csv(os.path.join(local_dir, "DTC_Trauma_Sheet_Phase_3.csv")) |
| |
| # casualty_id is a relative path under PHASE_3/ |
| labels["clip_path"] = labels["casualty_id"].apply( |
| lambda p: os.path.join(local_dir, "PHASE_3", p) |
| ) |
| print(labels[["casualty_id", "trauma_head", "clip_path"]].head()) |
| ``` |
|
|
| Phase 1 & 2 labels join on `video_id`, which matches the MP4 stem under `PHASE_1/`: |
|
|
| ```python |
| p12 = pd.read_csv(os.path.join(local_dir, "DTC_Trauma_Phase1and2.csv")) |
| p12["clip_path"] = p12["video_id"].apply( |
| lambda vid: os.path.join(local_dir, "PHASE_1", f"{vid}.mp4") |
| ) |
| ``` |
|
|
| The media files are tracked with Git LFS — run `git lfs install` first if you prefer a full `git clone` over `snapshot_download`. |
|
|
| ## Intended Uses |
|
|
| - Multi-view casualty detection and tracking |
| - Triage scene understanding from ground perspectives |
| - Multi-modal fusion (video + vitals + still imagery) |
| - Trauma severity classification and medic action recognition |
| - Simulated trauma response research |
|
|
| ## License |
|
|
| Restricted — contact dataset maintainers for usage terms. |
|
|
