InfoBayAI/egocentric_video

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

{
  "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 or contact us directly.

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