🎥 MoveMean — Natural Language Video Understanding Platform

🌟 Inspiration

We wanted to explore how machines can understand the meaning of videos — not just by detecting scenes or objects, but by truly interpreting the context and conversation within them. Our goal was to make it possible to talk with videos — to ask questions, get insights, and navigate directly to the moments that matter.

💡 What It Does

MoveMean lets users upload a video and interact with it using natural language.
You can ask:

“Where does the speaker talk about AI ethics?”
“Summarize the discussion about machine learning.”

The platform responds instantly, showing relevant timestamps, summaries, or answers — just like chatting with the video itself.

⚙️ How We Built It

We designed a serverless architecture to keep things scalable and cost-effective:

Authentication & API Layer
- Users authenticate through Amazon Cognito.
- Requests are routed securely via API Gateway.
Video Upload & Processing
- When a video is uploaded, it triggers an AWS Lambda function that:
  - Uses Amazon Transcribe for speech-to-text.
  - Sends results to S3 for storage.
  - Communicates asynchronously via SQS to manage load.
Analysis & Knowledge Storage
- Another Lambda controller processes both motion and transcript data.
- It stores structured insights into an Amazon Bedrock Knowledge Base.
- DynamoDB tracks job progress to prevent redundant executions.
Retrieval & Interaction
- When a user asks a question, the system:
  - Searches the Bedrock knowledge base using semantic search.
  - Runs an LLM inference (through Amazon Bedrock) on the retrieved chunks.
  - Returns the most relevant segments or responses.

🧩 Challenges We Ran Into

Managing asynchronous processing across multiple AWS services.
Handling large video files efficiently while staying within serverless limits.
Fine-tuning the retrieval pipeline to align transcription text with video timestamps accurately.

🏆 Accomplishments We’re Proud Of

Built a fully serverless, event-driven video understanding pipeline.
Integrated speech-to-text, semantic search, and LLM reasoning in one workflow.
Achieved real-time interaction with videos using natural language.

📚 What We Learned

How to orchestrate multiple AWS components (S3, Lambda, Bedrock, SQS, Cognito, DynamoDB) into a cohesive data pipeline.
Deepened our understanding of retrieval-augmented generation (RAG) and video-based AI workflows.
Learned how to handle asynchronous event-driven architectures for media intelligence use cases.

🚀 What's Next for MoveMean

Add multi-language transcription and translation support.
Integrate visual understanding models (e.g., scene detection, facial recognition).
Enable real-time video Q&A during live streams.
Develop a frontend dashboard for interactive video insights and summaries.