Multi-dimensional Analysis
Multi-dimensional Video Analysis
Deeptrain’s multi-dimensional analysis goes beyond simple frame extraction. It interprets video data across three primary axes: Temporal (time-based changes), Spatial (visual elements), and Contextual (metadata and audio). This allows AI agents to understand sequence-dependent actions, such as a process being performed in a tutorial or the progression of a narrative in a film.
Core Processing Workflow
The system treats video as a continuous data stream rather than a collection of static images. By utilizing the Transcribe API, Deeptrain converts these streams into a format digestible by LLMs, ensuring that the temporal context is preserved for training or RAG (Retrieval-Augmented Generation).
Using the Transcribe API
The Transcribe API is the primary interface for feeding video content into your AI models. It handles the extraction of visual features and audio transcriptions simultaneously.
Input Parameters
| Parameter | Type | Description |
| :--- | :--- | :--- |
| source | string | The URL (YouTube, Vimeo) or local file path to the video. |
| sampling_rate | float | Frequency of temporal snapshots (e.g., 1 snapshot per second). |
| include_audio | boolean | Whether to transcribe and sync audio with visual data. |
| dimension_depth | string | Level of detail for spatial analysis (standard or high). |
Example: Processing a Remote Video
from deeptrain import VideoProcessor
# Initialize the processor
processor = VideoProcessor(api_key="your_api_key")
# Analyze a video for AI training
video_data = processor.transcribe(
source="https://www.youtube.com/watch?v=example",
sampling_rate=0.5,
include_audio=True
)
# video_data now contains synchronized temporal embeddings
print(video_data.summary)
Temporal Data Integration
When processing video, Deeptrain generates a Temporal Context Map. This map ensures that when an AI agent queries a specific moment in the video, it understands what happened immediately before and after that timestamp.
- Sequential Encoding: Frames are encoded in sequence, allowing the LLM to perceive motion and change.
- Audio-Visual Sync: The Transcribe API aligns spoken words with specific visual frames, creating a multi-dimensional dataset that improves the accuracy of "vision-enabled" LLMs.
Usage in AI Training
Once a video is processed via the multi-dimensional analysis pipeline, the resulting data can be used to:
- Augment Context Windows: Provide your AI agent with a compressed "memory" of the video content.
- Fine-tune Multi-modal Models: Use the synchronized video/audio/text data to train custom models on specific domain knowledge (e.g., medical procedures or technical walkthroughs).
- Real-time Querying: Use the localized embedding database to ask questions like, "At what point in the video does the instructor plug in the cable?"
Supported Formats and Platforms
Deeptrain is designed to be platform-agnostic, supporting a wide range of video sources:
- Web Platforms: YouTube, Vimeo, and self-hosted MP4/WebM links.
- Local Storage: Direct uploads for private datasets.
- Live Streams: Real-time analysis for live data sources (Beta).