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ntdky/video-detections

Video Semantic Index Dataset Overview This dataset contains frame-level object detection results extracted from sampled video frames. Detections were generated using a YOLO-based object detection model and stored in Apache Parquet format for efficient structured indexing and downstream semantic retrieval. Each row represents a single detected object instance. File Information Filename: video_semantic_index.parquet Format: Apache Parquet… See the full description on the dataset page: https://huggingface.co/datasets/ntdky/video-detections.

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  • —# Video Semantic Index Dataset

## Overview

This dataset contains frame-level object detection results extracted from sampled video frames.

Detections were generated using a YOLO-based object detection model and stored in Apache Parquet format for efficient structured indexing and downstream semantic retrieval.

Each row represents a single detected object instance.

## File Information

  • —Filename: video_semantic_index.parquet
  • —Format: Apache Parquet
  • —Compression: SNAPPY
  • —Created with: pyarrow 18.1.0
  • —Total rows: 1,398
  • —Row groups: 1
  • —Total compressed size: 59,376 bytes

## Schema Description

Each row corresponds to a single object detection from a sampled video frame.

Column NameTypeDescription
video_idstringIdentifier of the source video
frame_indexint64Index of the sampled frame
timestamp_secint64Timestamp of the frame (in seconds)
class_labelstringDetected object category label
bounding_boxlist[double]Bounding box coordinates
confidence_scoredoubleDetection confidence score

## Bounding Box Format

Bounding boxes are stored as: 

  [xmin, ymin, xmax, ymax]
  • —Format: LIST logical type in Parquet
  • —Each detection contains exactly 4 floating-point values
  • —Total stored bounding box elements: 5,592

Values represent pixel coordinates in the original frame resolution.

## Dataset Statistics

### Frame Information

  • —Frame index range: 0 – 558
  • —Total unique frames: 559

### Timestamp

  • —Timestamp range: 0 – 2790 seconds

### Confidence Score

  • —Range: 0.30 – 0.981

### Class Labels

  • —Stored as UTF-8 strings
  • —Dictionary-encoded for storage efficiency
  • —No null values

## Storage Configuration

  • —Encoding: PLAIN / RLE / RLE_DICTIONARY
  • —Compression Codec: SNAPPY
  • —Dictionary pages enabled for string columns
  • —Single row group

## Data Generation Pipeline

  1. 1.Input video processed offline.
  2. 2.Frames sampled at fixed temporal intervals.
  3. 3.YOLO-based object detection model applied per frame.
  4. 4.Each detected object stored as one row.
  5. 5.Bounding boxes serialized as list[float64].
  6. 6.Data exported using PyArrow 18.1.0.
  7. 7.SNAPPY compression applied.

## Notes

  • —Each row represents a single detected object.
  • —Multiple rows may correspond to the same frame.
  • —No missing or null values are present.
  • —This dataset serves as the frame-level semantic index, which can be further aggregated into temporal segment-level indices for retrieval tasks.