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.
About Pranjul Rathour

Trophy and certificate after a win

Walking a room through evaluation criteria
Pranjul Rathour is a GenAI engineer from Kanpur, India, and CTO at SCULT INDIA, currently shipping production RAG,
fine-tuning and agentic AI systems, mentoring 200+ students through TechVerse Enclave, and judging and speaking at
student hackathons across India. Updated 2026-09-11.
Reach out if you want to talk GenAI, book a campus session, or invite him to judge:
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Pranjul Rathour · GenAI engineer, 3x hackathon winner, campus mentor. Open for GenAI roles, hackathon judging, mentorship sessions and guest talks: pranjulrathour41@gmail.com · Invite me to your campus
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