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Datasets:
Ragab-Adel
/
human-motion-skeleton-sample

Size:
n<1K
Tags:
computer-vision
human-pose-estimation
human-motion
activity-recognition
skeleton
License:
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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
camera_id: string
track_id: int64
movement_type: string
started_at: string
ended_at: string
duration_seconds: double
confidence: double
torso_angle_degrees: double
captured_at: string
body_height_frame_normalized: double
left_knee_angle_degrees: double
frame_number: int64
right_knee_angle_degrees: double
speed_normalized: double
motion_type: string
to
{'camera_id': Value('string'), 'track_id': Value('int64'), 'frame_number': Value('int64'), 'captured_at': Value('string'), 'motion_type': Value('string'), 'confidence': Value('float64'), 'speed_normalized': Value('float64'), 'torso_angle_degrees': Value('float64'), 'left_knee_angle_degrees': Value('float64'), 'right_knee_angle_degrees': Value('float64'), 'body_height_frame_normalized': Value('float64')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              camera_id: string
              track_id: int64
              movement_type: string
              started_at: string
              ended_at: string
              duration_seconds: double
              confidence: double
              torso_angle_degrees: double
              captured_at: string
              body_height_frame_normalized: double
              left_knee_angle_degrees: double
              frame_number: int64
              right_knee_angle_degrees: double
              speed_normalized: double
              motion_type: string
              to
              {'camera_id': Value('string'), 'track_id': Value('int64'), 'frame_number': Value('int64'), 'captured_at': Value('string'), 'motion_type': Value('string'), 'confidence': Value('float64'), 'speed_normalized': Value('float64'), 'torso_angle_degrees': Value('float64'), 'left_knee_angle_degrees': Value('float64'), 'right_knee_angle_degrees': Value('float64'), 'body_height_frame_normalized': Value('float64')}
              because column names don't match
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1880, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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camera_id
string
track_id
int64
frame_number
int64
captured_at
string
motion_type
string
confidence
float64
speed_normalized
float64
torso_angle_degrees
float64
left_knee_angle_degrees
float64
right_knee_angle_degrees
float64
body_height_frame_normalized
float64
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CAM-01
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End of preview.

YAML Metadata Warning:The task_categories "time-series" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

Human Motion Skeleton & Activity Dataset — Public Evaluation Sample

Release: human_motion_sample_2026_08_17_v2

A lightweight public evaluation sample of anonymized human motion data extracted from CCTV/RTSP video and converted into structured skeleton pose and motion metadata.

This public release is intentionally limited to help researchers, ML engineers, and AI teams evaluate the usefulness of the dataset before requesting access to a larger commercial dataset.

What's included

  • 500 skeleton pose records
  • 461 motion observations
  • 17 motion segments
  • Anonymous track IDs
  • Time-aware motion sequences
  • Automatically inferred activity labels
  • Technical quality and provenance metadata

Potential use cases

This sample may be useful for:

  • Human pose estimation research
  • Motion understanding
  • Activity recognition
  • Temporal movement analysis
  • Skeleton-based machine learning
  • Computer vision experimentation
  • Privacy-preserving motion analytics
  • AI model prototyping

Source

  • Source type: CCTV / RTSP
  • Camera ID: CAM-01
  • Source session: 20260816T102302Z

The public dataset contains structured motion information only.

Privacy

No raw CCTV imagery is included.

This sample does not contain:

  • Raw CCTV frames
  • Face crops
  • Face images
  • Face embeddings
  • Private event clips
  • Employee names
  • Personal identity fields

The dataset is designed around skeleton-only / structured motion representations.

Labels

Current motion labels are automatically inferred / pseudo-labeled.

They are not human-verified ground truth.

Current supported motion categories may include:

  • standing
  • walking
  • running
  • sitting
  • bending
  • falling
  • unknown

See docs/LABEL_DEFINITIONS.md for details.

Dataset files

Data

  • data/skeleton_pose.jsonl
  • data/motion_observations.jsonl
  • data/motion_segments.jsonl

Metadata

  • metadata/sample_manifest.json
  • metadata/source_manifest.json
  • metadata/source_provenance.json
  • metadata/source_quality_report.json

Documentation

  • docs/DATA_DICTIONARY.md
  • docs/LABEL_DEFINITIONS.md
  • docs/RIGHTS_AND_PROVENANCE.md

Sample limitation

This release contains a maximum of 500 skeleton records and represents only a small evaluation subset.

It should not be treated as the complete Human Motion Dataset.

Larger dataset availability

Larger datasets with substantially more motion observations and sessions may be made available separately for evaluation, research, or commercial licensing, subject to applicable rights and redistribution requirements.

If your team is working on:

  • Human motion models
  • Pose / activity recognition
  • Robotics
  • Computer vision
  • Behavior analytics
  • Skeleton-based AI

and would like to evaluate a larger sample or discuss dataset requirements, please contact the dataset publisher through the platform profile.

Release strategy

Public samples are expected to be updated regularly as additional validated motion data becomes available.

Each public release remains intentionally limited, while larger validated datasets are retained separately.

Important notice

Technical provenance and quality validation do not by themselves establish legal redistribution rights.

Any larger commercial release is subject to appropriate rights verification and licensing terms.
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