Project Overview
Objective
Developed an AI-powered video search and analysis system with scene detection and summarization capabilities.
Stack
FastAPIOpenCVCLIP (ViT-B/32)PyTorchFAISSYOLOv8Next.jsGPT-5GPT-4o-miniGPT-4.1
Delivery highlights
- Developed a video analysis application that allows users to upload videos and automatically analyze their content. The system extracts frames from videos, detects objects using YOLOv8, and generates visual embeddings with CLIP to represent each scene. These embeddings are stored in a FAISS vector index, enabling fast search and retrieval of relevant scenes based on objects or visual similarity. Built backend services with FastAPI for video upload, timeline generation, frame preview, and scene navigation, and integrated LLMs (GPT-5, GPT-4o-mini, GPT-4.1) to automatically summarize video timelines. A Next.js interface was developed to let users easily search scenes, preview frames, and jump directly to important timestamps in the video.
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How video search and scene analysis works
Video indexing creates searchable scenes; a later query retrieves matching moments and produces a concise timeline.Rendering workflow diagram…
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System workflow
Video indexing pipeline
Videos are segmented into frames, analyzed for objects and visual features, then indexed for semantic search.Rendering workflow diagram…
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System workflow
Scene retrieval and summarization
User queries are matched against scene embeddings; retrieved moments are summarized with LLM-generated descriptions.Rendering workflow diagram…
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System workflow
Source-reviewed processing sequence
A conservative end-to-end boundary taken from the source audit. Specific endpoints, storage writes, and external calls are only claimed in the project-specific diagrams when the implementation confirms them.Rendering workflow diagram…
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