Technical Documentation for the Text-to-Video Dataset “VidData”

1. Introduction

This dataset contains 1006 annotated videos of everyday scenes, used for training and evaluating AI models in video generation and recognition. It is structured to meet the needs of Text-to-Video models and motion analysis.

2. Dataset Specifications

2.1. Generation Criteria

Maximum video duration: 10 seconds maximum
Video themes:
- Walking
- Exercising
- Writing
- Shopping
- Sleeping
- Meditating
- Working
- Studying
- Driving
- Washing
- Gardening
- Calling
- Listening
- Organizing
- Planning
- Relaxing
- Teaching
Video size: 512×512 pixels

2.2. Dataset Organization

The dataset is organized under a main folder called VidData, which includes three essential parts: data/train/: Contains a VidData.csv file, likely storing metadata or structured details about the videos. video/: Holds the video files (e.g., ---_iRTHryQ_13_0to241.mp4), named in a specific format, possibly indicating segments or unique identifiers. readme.md: Provides documentation about the dataset's structure and usage. This structure clearly separates raw video data, metadata (CSV), and documentation, ensuring efficient organization for analysis and processing. data/train/: Contains CSV files with video-related metadata. video/: Stores the actual video files.

3. Data Structure

The dataset is stored as a CSV file and includes the following columns:

Column	Type	Description
video	string	Video file name
caption	string	Textual description of the video
temporal consistency score	float64	Temporal consistency score
fps	float64	Frame per second
frame	int64	Number of frames in the video
seconds	float64	Video duration in seconds
motion score	float64	Motion score
camera motion	string	Type of camera motion (e.g., pan_left)

4. Libraries Used

4.1. Library Examples

Here are some example libraries that can be used when analyzing this data:

OpenCV: Video manipulation and processing (reading, writing, frame extraction, contour detection, filtering, etc.).
Scikit-Image: Calculating the Structural Similarity Index (SSIM) for image quality evaluation and various image transformations (segmentation, filtering, etc.).
NumPy: Efficient manipulation of matrices and arrays, essential for calculations on images and videos.
Pandas: Managing and structuring metadata associated with videos (e.g., file names, timestamps, annotations).
Matplotlib/Seaborn: Visualizing analysis results as graphs.

4.2. Installing Dependencies

Follow the instructions below to install the required libraries:

Create a requirements.txt file and add the following: opencv-python==4.8.1.78 # Video manipulation and processing scikit-image==0.22.0 # SSIM calculation and image transformations numpy==1.26.2 # Efficient manipulation of matrices and arrays pandas==2.1.4 # Managing and structuring metadata matplotlib==3.8.2 # Visualizing analysis results seaborn==0.12.2 # Advanced visualization with enhanced graphics
Run the command: pip install -r requirements.txt

Note: Only include the libraries you need in requirements.txt.

5. Using the Dataset

5.1. Primary Applications

5.1.1. Text-to-Video Generation

Train models to generate video based on textual input.
Benchmark performance by comparing generated video against dataset entities.

5.1.2. Video Description Models

Evaluate models designed to generate textual descriptions from videos.

5.1.3. Temporal Consistency Analysis

Test model for maintaining smoothness and coherence in video generation.

5.2. Example Workflow

Load the dataset using Python:

4. Libraries Used

import pandas as pd

dataset = pd.read_csv('VidData.csv')
print(dataset.head())

##Access video metadata:
video = dataset.iloc[0]  # First entry
print(f"Video Name: {video['video_name']}")
print(f"Caption: {video['Caption']}")
print(f"Duration: {video['duration_seconds']} seconds")

##Filter video based on motion:
high_motion_videos = dataset[dataset['motion_score'] > 1.0]
print(high_motion_videos)

6. File Format

The dataset is delivered in CSV format, with each column representing a video and its metadata.

7. Sample Entry:

video_name	caption	temporal_consistency_score	fps	frames	duration_seconds	motion_score	camera_motion
E_1.mp4	The video shows a soccer player kicking a soccer ball.	0.948826	30	195	6.5	0.826522	1.105807

8. Contact

For inquiries, please contact:

Email: [email protected]
Website: databoost.us