README.md

---
license: cc-by-nc-4.0
language:
- en
tags:
- video
- activity-recognition
- temporal-localization
- activity-state-transition
- robotics
pretty_name: ASTime
size_categories:
- 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train.parquet
---


# ASTime

![ASTime img](./ASTime.png)

ASTime, short for **Activity State Transition Timestamp**, is a video dataset for studying real-time perception of human activity changes from a robotic perspective. Instead of only providing the start and end time of an activity segment, ASTime explicitly annotates the **transition timestamp**: the moment at which a new human activity state becomes semantically distinguishable from the previous state.

The released dataset contains anonymized, muted, untrimmed long videos collected in constructed home-like laboratory environments, together with JSON annotations for activity-state transition timestamp localization. The train/validation/test split is subject-disjoint to evaluate generalization to unseen participants.

## 📌 Dataset Summary

![ASTime Dataset Statistical Overview](./dataset_statistics.png)
*Statistical overview of the ASTime dataset. (A) The dataset scale and train/val/test split summarize the videos, participants, recorded time, annotated events, atomic actions, and activity labels. (B) The atomic action distribution presents the frequency of fine-grained activity categories. (C) The human-state coverage groups state-specific action occurrences into four semantic domains.*

<br>

The statistics below are computed from the files in this repository under `Videos/` and `Annotations/`.

| Split | Subjects | Videos | Transition events | Duration | Frames |
|---|---:|---:|---:|---:|---:|
| Train | 12 | 116 | 2,662 | `12:12:24.438` | 1,317,131 |
| Validation | 2 | 12 | 257 | `01:08:31.678` | 123,227 |
| Test | 2 | 33 | 545 | `02:37:54.942` | 283,964 |
| Total | 16 | 161 | 3,464 | `15:58:51.058` | 1,724,322 |

Quick file facts:

- 🎞️ Video format: MP4 / H.264.
- 🔇 Audio: no audio streams are present in the released videos.
- 🖼️ Resolution and frame rate: most videos are `1920 x 1080` at about `30 FPS`; two videos are `3840 x 2160` at `30 FPS`.



Label vocabulary:

- 3 basic postures: `standing`, `sitting`, and `lying`.
- 18 non-posture action terms, such as `reading`, `using a phone`, `falling`, `stomachache`, and `carrying heavy objects`.
- Composite labels combine a posture with one or more action terms, for example `sitting, using a phone` or `sitting, using a phone, eating`.

## 🗂️ Dataset Structure

The dataset has two main components: video files and JSON annotations.

| Component | Path pattern | Description |
|---|---|---|
| 🎬 Training videos | `Videos/train/P001_train/*.MP4` ... `Videos/train/P013_train/*.MP4` | Untrimmed videos for the training subjects. |
| 🎬 Validation videos | `Videos/validation/P012_validation/*.MP4`, `Videos/validation/P014_validation/*.MP4` | Untrimmed videos for the validation subjects. |
| 🎬 Test videos | `Videos/test/P015_test/*.MP4`, `Videos/test/P016_test/*.MP4` | Untrimmed videos for the held-out test subjects. |
| 🏷️ Training labels | `Annotations/ASTime_Annotations_train/annotation_train_labels/*.json` | Standard activity-state transition labels for training videos. |
| 🏷️ Validation labels | `Annotations/ASTime_Annotations_validation/annotation_validation_labels/*.json` | Standard activity-state transition labels for validation videos. |
| 🏷️ Test labels | `Annotations/ASTime_Annotations_test/annotation_test_label/*.json` | Standard activity-state transition labels for test videos. |
| 💬 Training captions | `Annotations/ASTime_Annotations_train/annotation_train_captioned/*.json` | Auxiliary natural-language captioned annotations for training videos. |
| 💬 Validation captions | `Annotations/ASTime_Annotations_validation/annotation_validation_captioned/*.json` | Auxiliary natural-language captioned annotations for validation videos. |

The `data/` directory contains a normalized Parquet representation of the training caption annotations for Hugging Face Dataset Viewer visualization. The original videos and JSON annotation files remain the canonical source files under `Videos/` and `Annotations/`, respectively.

Naming follows the same anonymized subject/video ID convention across videos and annotations:

| Item | Example |
|---|---|
| Subject folder | `P001_train`, `P012_validation`, `P015_test` |
| Video file | `P001_train_1-1.MP4` |
| Standard label file | `P001_train_1-1.json` |
| Video ID / JSON key | `P001_train_1-1` |

The subject IDs are anonymized as `P001` through `P016`. The training split contains `P001` to `P011` and `P013`, the validation split contains `P012` and `P014`, and the testing split contains `P015` and `P016`.

## 📝 Annotation Format

Annotations are stored as JSON files. Each JSON file contains a single top-level key, which is the video ID. The value is a list of transition-event annotations.

### Video-Annotation Matching

Use the shared video ID, i.e. the file stem without extension, to locate the corresponding standard annotation file.

| Video | Annotation |
|---|---|
| `Videos/train/P001_train/P001_train_1-1.MP4` | `Annotations/ASTime_Annotations_train/annotation_train_labels/P001_train_1-1.json` |
| `Videos/validation/P012_validation/P012_validation_1-1.MP4` | `Annotations/ASTime_Annotations_validation/annotation_validation_labels/P012_validation_1-1.json` |
| `Videos/test/P015_test/P015_test_1-1.MP4` | `Annotations/ASTime_Annotations_test/annotation_test_label/P015_test_1-1.json` |

### Standard Label Example

Standard label files use compact activity-state labels in the `text` field:

```json
{
  "P001_train_1-1": [
    {
      "t1_caption": "",
      "start_time": 8.0,
      "timestamp": 11.0,
      "end_time": 19.0,
      "text": "sitting, using a laptop",
      "flag": 0
    }
  ]
}
```

### Captioned Annotation Example

The training and validation splits also provide auxiliary captioned annotations in `annotation_train_captioned/` and `annotation_validation_captioned/`. In these files, `t1_caption` describes the previous state and `text` describes the target state in natural language:

```json
{
  "P001_train_1-1": [
    {
      "t1_caption": "This person is sitting.",
      "start_time": 8.0,
      "timestamp": 11.0,
      "end_time": 19.0,
      "text": "The person is sitting on a couch and using a laptop.",
      "flag": 0
    }
  ]
}
```

Field definitions:

| Field | Type | Description |
|---|---|---|
| `t1_caption` | string | Previous-state natural-language caption when available. This field is empty in the standard label annotations and populated in the auxiliary training and validation caption files. |
| `start_time` | float | Start time, in seconds, of the temporal window associated with the transition event. |
| `timestamp` | float | Activity-state transition timestamp, in seconds. This is the target timestamp to localize. |
| `end_time` | float | End time, in seconds, of the temporal window associated with the transition event. |
| `text` | string | Target activity-state label or natural-language target-state caption, depending on the annotation directory. |
| `flag` | integer | Auxiliary annotation flag retained from the annotation process. In the current release, 3,408 events have `flag = 0` and 56 events have `flag = 1`. |

For the main activity-state transition timestamp task, use:

- Training labels: `Annotations/ASTime_Annotations_train/annotation_train_labels/`
- Validation labels: `Annotations/ASTime_Annotations_validation/annotation_validation_labels/`
- Test labels: `Annotations/ASTime_Annotations_test/annotation_test_label/`

For the auxiliary natural-language captioned representation, use:

- Training captions: `Annotations/ASTime_Annotations_train/annotation_train_captioned/`
- Validation captions: `Annotations/ASTime_Annotations_validation/annotation_validation_captioned/`


All standard label annotations satisfy:

```text
start_time <= timestamp <= end_time
```



## 🛠️ Dataset Construction and Collection Protocol

### Environment and Setup
We constructed a comprehensive simulated home environment within a laboratory to serve as the recording space. The setup comprises eight distinct functional rooms, including three living rooms, one bedroom, one kitchen, two study rooms, and one conference room. To introduce spatial variance, the internal furniture layout within each room was randomly adjusted across different recording sessions. The lighting conditions captured natural variations, encompassing both daytime and nighttime illumination. Regarding the recording equipment, we avoided static surveillance setups. For every specific scene, two cameras were dynamically deployed at different positions and viewing angles, specifically chosen to ensure clear capture of the human activities without adhering to a fixed coordinate system.

### Participants and Execution
The dataset features 16 healthy recruited university students aged between 22 and 31, comprising 14 males and 2 females. All participants reported no physical or mobility impairments, ensuring baseline kinematic consistency across the recorded routines. Furthermore, they wore their own normal daily clothing to introduce natural visual variance, and rigorous decency standards were maintained throughout all sessions. Crucially, there were no rehearsals prior to the recordings to avoid rigid or performative movements. Rather than executing isolated and mechanized actions, participants were instructed to naturally transition between multiple randomly assigned activities, ensuring long and continuous untrimmed video sequences.

### Mitigating Observer Bias via Blind Protocol
To ensure the ecological validity of the collected data and minimize potential observer effects, we employed a task-oriented blind protocol during the recording process:
* **Blinded to Measurement Target:** Participants were informed that the study aimed to evaluate general robotic perception capabilities rather than specific timing accuracy or transition timestamp detection. This minimized demand characteristics, preventing participants from intentionally exaggerating or pausing at activity boundaries.
* **High-Level Directives:** Participants were not given micro-level kinematic step-by-step scripts. Instead, they received high-level task directives. For example, rather than instructing a participant to *walk to the sofa, sit down, wait three seconds, and then pick up the book*, the blind directive simply stated *go read a book in the living room for a while*. Similarly, for safety-critical events, the instruction was *simulate a sudden stomachache while cleaning* rather than dictating specific falling mechanics. This approach preserved the natural diversity of movement execution, ensuring that postural transitions and intermediate hesitations reflected natural human behavioral dynamics.

### Annotation Protocol and Boundary Disambiguation
To balance annotation efficiency and precision, we implemented a two-stage annotation protocol:
1. **Real-Time Initial Localization:** During data collection, a custom real-time logging tool recorded the preliminary start and end times of each activity sequence.
2. **Multi-Annotator Fine-Grained Alignment:** Three independent graduate researchers trained in video analysis conducted post-hoc refinement to ensure the objectivity of the novel transition timestamp.

When facing ambiguous boundaries (e.g., the transition between reaching for a book and reading), the annotators adhered to a strict semantic establishment criterion. The transition timestamp was defined as the exact moment when the new behavioral state was fully established and visually distinguishable from the preceding state, rather than the moment of the preliminary preparatory movement. To evaluate inter-annotator agreement, we utilized a temporal tolerance window. An agreement was considered valid if the transition timestamps provided by the three independent annotators fell within a 0.5-second margin (15 frames), in which case the average timestamp was adopted. In cases where the variance exceeded this threshold or semantic disagreements occurred, a senior researcher acted as an arbitrator to review the video frame-by-frame and determine the final deterministic boundary.


## 📊 Activity-State Statistics

### Posture Distribution

| Posture | Train | Validation | Test | Total |
|---|---:|---:|---:|---:|
| standing | 1,207 | 129 | 223 | 1,564 |
| sitting | 1,145 | 112 | 230 | 1,487 |
| lying | 310 | 16 | 92 | 418 |

### Atomic Action-Term Distribution

Counts in this table are computed by splitting composite labels after the posture term. Multi-action labels therefore contribute to multiple action terms.

| Action term | Count |
|---|---:|
| using a phone | 448 |
| drinking | 384 |
| reading | 370 |
| eating | 271 |
| writing | 217 |
| using a laptop | 216 |
| carrying heavy objects | 184 |
| taking off jacket | 184 |
| putting on a jacket | 180 |
| headache | 168 |
| stomachache | 162 |
| pouring | 149 |
| picking up | 129 |
| reaching elevated objects | 116 |
| falling | 66 |
| washing | 58 |
| operating a microwave | 52 |
| making coffee | 46 |



## 🎯 Intended Use

ASTime is intended for research on:

- activity-state transition timestamp localization;
- human activity recognition in long, untrimmed videos;
- robotic perception of human state changes;
- temporal understanding of daily-living, object-manipulation, information-interaction, and health/safety-related activities.

The subject-disjoint split should be used when evaluating generalization to unseen people.

## 🔒 Privacy and Ethics

We strictly adhered to high ethical standards to protect participant privacy during data collection. The implemented privacy protection framework includes the following core aspects:

* **Constructed Simulated Environment:** To address privacy concerns, data collection was conducted in a laboratory environment designed to accurately simulate home settings, including bedrooms, rather than in actual private residences.
* **Informed Consent and Licensing Transparency:** Written informed consent was obtained from all participants, who retained the right to withdraw their data at any time without penalty. Furthermore, participants were fully informed and explicitly agreed to the complete licensing scope and permissible usage boundaries of the ASTime dataset.
* **Strict Decency Standards:** We enforced rigorous decency protocols, ensuring all participants were fully clothed. The simulated bedroom scenarios were strictly limited to functional activities relevant to elder care monitoring, such as resting or reading in bed, and no intimate or private activities were recorded.
* **Comprehensive Data Anonymization:** We implemented strict anonymization procedures across all data modalities before analysis. Facial information was blurred to prevent identity identification. All video files underwent complete audio muting to completely remove conversations and ambient sounds. Consequently, all processed data were utilized solely for algorithmic analysis without being publicly linked to participant identities.

*(Users should not attempt to identify participants or reverse anonymization. The dataset should be used only in ways consistent with the repository license and data-use terms.)*