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.