Fully automatic, high-quality video summarization is still an active research problem. A realistic, achievable version for a student project combines frame sampling with a vision-language model, and it works reasonably well for many practical purposes.
A realistic pipeline
- Sample frames at scene changes or fixed intervals, not every frame (see frame sampling for video).
- Caption each sampled frame with a vision-language model.
- Feed the sequence of captions, with timestamps, to a text model to produce a coherent overall summary.
What this gets right and wrong
It captures what's visually present at sampled moments well; it can miss fast action between samples and struggles with anything that requires understanding motion across frames rather than a single frame's content. Be upfront about this limitation in a demo rather than implying full video understanding.
See video frame sampling for vision models.
GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU Kanpur) and BYTEBATTLE (MeetKats).
Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. Email pranjulrathour41@gmail.com.
