HA-Ego-Samples
Curated egocentric video dataset for robotics and manipulation research. Contains 501.1 hours of first-person footage across 5,514 unique persons performing manual tasks in commercial and residential environments.
Dataset Overview
| Metric |
Value |
| Total persons |
5,514 |
| Total video segments |
10,598 |
| Total duration |
501.1 hours |
| Total size |
~2.5 TB |
| Resolution |
1920×1080 |
| Codec |
H.264 |
| Frame rate |
30 FPS |
| Audio |
None |
| Clip duration range |
30s – 600s |
| Unique environments |
181 |
| Unique tasks |
756 |
Directory Structure
s3://500hr-samples/
├── commercial/
│ ├── factory/ # 1,133 persons · 2,021 segments · 107.4 hours
│ │ ├── person{N}/
│ │ │ ├── person{N}_segments.json
│ │ │ ├── person{N}_segment1.mp4
│ │ │ ├── person{N}_segment2.mp4
│ │ │ └── ...
│ │ └── ...
│ └── hospitality/ # 3,567 persons · 7,352 segments · 340.3 hours
│ ├── person{N}/
│ │ └── ...
│ └── ...
├── residential/ # 814 persons · 1,225 segments · 53.5 hours
│ ├── person{N}/
│ │ └── ...
│ └── ...
└── README.md
Split Summary
| Split |
Persons |
Segments |
Hours |
% of Total |
| commercial/factory |
1,133 |
2,021 |
107.4 |
21.4% |
| commercial/hospitality |
3,567 |
7,352 |
340.3 |
67.9% |
| residential |
814 |
1,225 |
53.5 |
10.7% |
| Total |
5,514 |
10,598 |
501.1 |
100% |
Per-Person JSON Schema
Each person folder contains a person{N}_segments.json file with the following schema:
{
"person_id": "person123",
"total_segments": 3,
"total_duration_sec": 456.0,
"segments": [
{
"person_id": "person123",
"video_index": "segment1",
"duration_sec": 152.0,
"task": "cutting_vegetables",
"environment": "commercial_kitchen",
"width": 1920,
"height": 1080,
"fps": 30.0,
"size_bytes": 245678901,
"codec": "h264"
}
]
}
Field Descriptions
| Field |
Type |
Description |
person_id |
string |
Unique person identifier (matches folder name) |
total_segments |
int |
Number of video segments for this person |
total_duration_sec |
float |
Sum of all segment durations in seconds |
segments[].video_index |
string |
Segment identifier (segment1, segment2, ...) |
segments[].duration_sec |
float |
Duration in seconds (verified by ffprobe) |
segments[].task |
string |
Activity label (e.g., washing_dishes, assembling_parts) |
segments[].environment |
string |
Environment label (e.g., commercial_kitchen, factory_floor) |
segments[].width |
int |
Video width in pixels |
segments[].height |
int |
Video height in pixels |
segments[].fps |
float |
Frames per second |
segments[].size_bytes |
int |
File size in bytes |
segments[].codec |
string |
Video codec |
Top 30 Environments
| Environment |
Clips |
Hours |
| commercial_kitchen |
3,685 |
175.7 |
| factory_floor |
1,122 |
59.1 |
| workshop |
1,090 |
57.9 |
| kitchen |
1,037 |
42.5 |
| warehouse |
484 |
23.7 |
| restaurant |
464 |
23.3 |
| hotel_room |
264 |
9.6 |
| bathroom |
250 |
9.0 |
| office |
176 |
9.0 |
| outdoor_area |
172 |
8.3 |
| home |
93 |
2.6 |
| home_kitchen |
86 |
3.8 |
| cafeteria |
83 |
2.9 |
| bar |
75 |
2.3 |
| banquet_hall |
72 |
3.5 |
| laundry_room |
71 |
4.0 |
| hallway |
62 |
3.2 |
| lobby |
61 |
3.4 |
| bedroom |
60 |
2.7 |
| hotel_lobby |
55 |
2.1 |
| balcony |
53 |
2.1 |
| auto_shop |
48 |
1.8 |
| living_room |
45 |
2.3 |
| cafe |
35 |
1.1 |
| restaurant_patio |
35 |
1.5 |
| conference_room |
31 |
1.3 |
| residential_room |
31 |
1.6 |
| auto_repair_shop |
29 |
0.9 |
| dining_hall |
26 |
1.2 |
Top 30 Tasks
| Task |
Clips |
Hours |
| washing_dishes |
794 |
32.8 |
| wiping_surface |
774 |
32.6 |
| cutting_vegetables |
634 |
33.7 |
| cooking_food |
373 |
16.4 |
| packaging_items |
267 |
11.8 |
| assembling_parts |
264 |
14.1 |
| assembling_footwear |
256 |
13.0 |
| frying_food |
237 |
9.6 |
| portioning_food |
234 |
9.9 |
| operating_machine |
180 |
10.2 |
| mopping_floor |
170 |
8.9 |
| sorting_produce |
150 |
8.0 |
| sweeping_floor |
145 |
6.5 |
| moving_objects |
136 |
4.8 |
| cleaning_floor |
134 |
6.2 |
| folding_clothes |
134 |
4.1 |
| kneading_dough |
119 |
4.4 |
| plating_food |
112 |
5.3 |
| serving_food |
106 |
3.9 |
| washing_mop |
103 |
3.8 |
| arranging_containers |
100 |
4.0 |
| eating_food |
95 |
2.6 |
| scrubbing_surface |
94 |
4.2 |
| preparing_food |
92 |
5.5 |
| clearing_table |
87 |
3.5 |
| packaging_food |
86 |
4.5 |
| polishing_glassware |
72 |
2.4 |
| servicing_machinery |
70 |
2.9 |
| making_bed |
65 |
2.7 |
Source
Videos are derived from egocentric recordings captured with chest-mounted cameras at 1920×1080, 30 FPS. Original footage was processed through a QA pipeline for activity segmentation and quality scoring, then cut into individual segments.
