ASTime

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/test split is subject-disjoint to evaluate generalization to unseen participants.

📌 Dataset Summary

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

Split	Subjects	Videos	Standard annotation JSON files	Transition events	Duration	Frames
Train	14	128	128	2,924	13:20:56.116	1,440,358
Test	2	33	33	545	02:37:54.942	283,964
Total	16	161	161	3,469	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/P014_train/*.MP4	Untrimmed videos for the training 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.
🏷️ 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.

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

Item	Example
Subject folder	P001_train, 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 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/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:

{
  "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 split also includes an auxiliary captioned version in annotation_train_captioned/. In these files, t1_caption describes the previous state and text describes the target state in natural language:

{
  "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 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, 3,413 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/
• Test labels: Annotations/ASTime_Annotations_test/annotation_test_label/

The directory Annotations/ASTime_Annotations_train/annotation_train_captioned/ provides an auxiliary natural-language captioned version of the training annotations. In those files, text is a natural-language target-state caption and t1_caption describes the previous state.

All standard label annotations satisfy:

start_time <= timestamp <= end_time

📊 Activity-State Statistics

Posture Distribution

Posture	Train	Test	Total
standing	1,341	223	1,564
sitting	1,257	230	1,487
lying	326	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
posture only	175
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

The released videos are anonymized and muted. Faces are blurred, and the video files contain no audio streams. Data collection was conducted in constructed home-like laboratory environments rather than private residences. Participants provided informed consent for dataset collection and release under the dataset's permitted usage terms.

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.