| # 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: | |
| 1. 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 | |
| 2. 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 | |
| ```python | |
| 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]](mailto\:[email protected]) | |
| - **Website**: [databoost.us](https://databoost.us) |