#### NOTE
This is a **Hugging Face dataset**. For large datasets, ensure `huggingface_hub>=1.1.3` to avoid rate limits. Learn more in the Hugging Face integration docs.
# Dataset Card for meva_mevid
This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 201 samples.
## Installation
If you haven’t already, install FiftyOne:
```bash
pip install -U fiftyone
```
## Usage
```python
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("Voxel51/extended_video_activities_drop_4")
# Launch the App
session = fo.launch_app(dataset)
```
# Dataset Card — MEVA Drop-4 / MeVID (`meva_mevid`)
MEVA drop-4-hadcv22 is a 201-clip subset of the [Multiview Extended Video with
Activities (MEVA)](https://mevadata.org) Known Facility 1 dataset. It was
released specifically to support the
[HADCV22 Self-Reported Leaderboard Challenge](https://actev.nist.gov/SRL) and
the broader ActEV activity-detection evaluation series run by NIST. The
[MeVID](https://arxiv.org/abs/2211.04843) (Multi-view Extended Video ID)
person re-identification benchmark builds its test/query annotations on top of
this same footage.
## Dataset Details
### Collection
The footage was recorded at the Muscatatuck Urban Training Center (MUTC) with
over 100 actors performing scripted scenarios. It was collected by Kitware Inc.
under the IARPA Deep Intermodal Video Analytics (DIVA) program.
| Property | Value |
|-----------------|-------------------------------------|
| Clips | 201 |
| Total duration | ~16.75 hours |
| Clip length | ~5 minutes each |
| Resolution | 1920 × 1080 |
| Frame rate | 30 fps |
| Codec | H.264 (remuxed to MP4 for FiftyOne) |
| Cameras | 19 fixed ground cameras |
| Recording dates | 7 days (March–May 2018) |
| Scenes | hospital, school, bus |
### Annotations (MeVID)
Person re-identification tracklets from the
[MeVID benchmark](https://arxiv.org/abs/2211.04843). Each tracklet records that
a specific person (identified by a cross-camera integer ID) was visible in a
specific clip for a given number of consecutive frames.
| Split | Clips with tracklets | Tracklets | Unique persons |
|-----------|------------------------|-------------|------------------|
| train | 77 | 657 | — |
| test | 64 | 499 | — |
| unlabeled | 60 | 0 | — |
| **total** | **141** | **1 156** | **155** |
317 of the 499 test tracklets are designated **query** tracklets for the
re-identification evaluation (matching a query sequence against a gallery of
test sequences across cameras and time).
> **Coverage**: drop-4 accounts for roughly 14% of the full MeVID annotation.
> The remaining 86% of MeVID tracklets reference cameras from other MEVA drops
> not included in this download and are silently ignored by the parser.
The MeVID annotation provides **tracklet-level metadata only** — it does not
include per-frame bounding boxes, absolute frame offsets within a clip, or
activity labels. The MEVA dataset does publish separate DIVA-format activity
annotations with bounding boxes (available via the [MEVA data repo](https://gitlab.kitware.com/meva/meva-data-repo)),
but those are a separate download not included here.
---
## FiftyOne Dataset Structure
Each of the 201 samples corresponds to one 5-minute MP4 clip.
### Sample-level fields
| Field | Type | Description |
|--------------------|------------|------------------------------------------------------------------------------------------------------------------|
| `filepath` | str | Absolute path to the `.mp4` file |
| `recording_date` | str | Recording date, e.g. `"2018-03-12"` |
| `start_time` | str | Clip start time, e.g. `"12:20:04"` |
| `end_time` | str | Clip end time, e.g. `"12:25:04"` |
| `scene` | str | Camera location: `"school"`, `"hospital"`, or `"bus"` |
| `camera_id` | int | Numeric camera ID, e.g. `424` |
| `camera_name` | str | Camera label used in MEVA filenames, e.g. `"G424"` |
| `temporal_segment` | int | 0-based index of this clip within its camera’s chronological sequence (matches the `T` value in MeVID filenames) |
| `split` | str | `"train"`, `"test"`, or `"unlabeled"` |
| `has_query` | bool | `True` if at least one query tracklet originates from this clip |
| `person_ids` | list[int] | Sorted list of unique person IDs visible in this clip per MeVID |
| `num_tracklets` | int | Total annotated tracklets in this clip |
| `tracklets` | list[dict] | One dict per tracklet — see below |
### Tracklet dict schema
Each entry in the `tracklets` list describes one contiguous person track:
| Key | Type | Description |
|-----------------|--------|--------------------------------------------------------------------------------------------------|
| `person_id` | int | Cross-camera person identity (shared across all clips) |
| `occurrence_id` | int | Which occurrence this is for this person (a person may appear multiple times across the dataset) |
| `frame_count` | int | Number of frames in the track |
| `split` | str | `"train"` or `"test"` |
| `is_query` | bool | Whether this tracklet is a re-ID query |
### Filtering examples
```python
import fiftyone as fo
ds = fo.load_dataset("meva_mevid")
# All test clips that contain a query tracklet
queries = ds.match(
(fo.ViewField("split") == "test") & fo.ViewField("has_query")
)
# Hospital clips only
hospital = ds.match(fo.ViewField("scene") == "hospital")
# Clips from a specific camera
cam424 = ds.match(fo.ViewField("camera_name") == "G424")
# Clips where person 212 appears
person212 = ds.match(fo.ViewField("person_ids").contains(212))
```
---
## Source & License
- **Videos**: [mevadata.org](https://mevadata.org) — licensed under
[CC BY 4.0](http://creativecommons.org/licenses/by/4.0)
- **MeVID annotations**: distributed with the MeVID benchmark
- **Produced by**: Kitware Inc. and IARPA
## Citation
```bibtex
@InProceedings{Corona_2021_WACV,
author = {Corona, Kellie and Osterdahl, Katie and Collins, Roderic and Hoogs, Anthony},
title = {MEVA: A Large-Scale Multiview, Multimodal Video Dataset for Activity Detection},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {January},
year = {2021},
pages = {1060-1068}
}
```