README.md

---
license: cc-by-4.0
size_categories:
- 1K<n<10K
language:
- en
tags:
- video
- egocentric-video
- first-person-video
- computer-vision
- video-understanding
- action-recognition
- multimodal
- vision-language
- embodied-ai
- robotics
- temporal-reasoning
- self-supervised-learning
task_categories:
- video-classification
---

**Dataset Description:**

**This dataset is a large-scale collection of egocentric video data, containing 100,000 hours of video, designed to support the development and training of advanced computer vision, video understanding, action recognition, and multimodal AI systems.**

Additionally, this dataset can be used in pipelines for Supervised Fine-Tuning (SFT) and Reinforcement Learning with Human Feedback (RLHF) workflows, improving model performance in temporal reasoning, activity recognition, scene understanding, and real-world embodied AI tasks.

**Video Refining Process**
To ensure enterprise-grade quality and usability, the dataset undergoes a comprehensive 8-step video refining and processing pipeline before final delivery:

1. **Duplicate Asset Elimination** — Removal of duplicate or repeated video assets to maintain dataset uniqueness, consistency, and high-quality training data.
2. **Vertical Format Validation** — Verification that all video content meets vertical format compliance standards for mobile-first AI training requirements.
3. **AV1 Codec Audit** — Standardization of video codec to H.264 to ensure broad compatibility and consistent playback across platforms and models.
4. **Intelligent Text Recognition** — Automated ITR processing to detect, extract, and handle on-screen text within video frames for cleaner training data.
5. **Animated Content Classification** — Type tagging and classification of animated vs. real-world content to enable accurate dataset segmentation.
6. **AI-Synthetic Media Detection** — Authenticity filtering to identify and flag AI-generated or synthetic video content, ensuring real-world data integrity.
7. **Brand Logo Intelligence** — BLI detection to identify, track, and manage brand logos within video frames for compliance and neutrality.
8. **Watermark Integrity Analysis** — Detection and removal of watermarks to deliver clean, unobstructed video output ready for AI model training.

This processing pipeline ensures that the dataset is clean, scalable, production-ready, and optimized for video AI, computer vision, multimodal AI, SFT, and RLHF workflows.

**Dataset Specification**

    Duration: 1,00,000 Hours
    Modality: Video (Audio + Visual)
    Format: Egocentric / First-Person Perspective
    Type: Human Activity & Real-World Interaction


**Key Use Cases**
    
    Egocentric human activity understanding
    First-person action recognition
    Human-object interaction analysis
    Video captioning and summarization
    Multimodal recommendation and reasoning systems

**Value of This Dataset**

    Enables advanced AI training for egocentric and wearable vision systems
    Improves multimodal understanding of human actions and environments
    Supports AI systems for contextual reasoning, captioning, and summarization
    Helps build intelligent assistive technologies and embodied AI systems
    Enhances performance in video-language and multimodal reasoning tasks


**Basic Json Schema**
```json
{
  "file_name": "string",
  "format": "string",
  "duration_seconds": "float",
  "width": "int32",
  "height": "int32",
  "fps": "float",
  "video_codec": "string",
  "audio_codec": "string",
  "bitrate": "int64",
  "file_size_bytes": "int64"
}
```


**Data Creation**

Procured through formal agreements and generated in the ordinary course of business.


**Considerations**

This dataset is provided for research and educational purposes only. It contains only sample data. For access to the full dataset and enterprise licensing options, please visit our website [InfoBay.AI](https://infobay.ai/) or contact us directly.

    -Ph: (91) 8303174762
    -Email: datareq@infobay.ai