# Faster than real-time detection of shot boundaries, sampling structure and dynamic keyframes in video

arXiv: https://arxiv.org/abs/2502.09202 · HTML: https://mlsanity.com/paper/2502.09202/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 8 references that are in the map.
- First posted on arXiv 13 Feb 2025.

## Builds on (most cited first, 4 of 4)

- [TransNet V2: An effective deep network architecture for fast shot transition detection](https://mlsanity.com/paper/2008.04838/) (2020)
- [Fast Optical Flow using Dense Inverse Search](https://mlsanity.com/paper/1603.03590/) (2016)
- [AutoShot: A Short Video Dataset and State-of-the-Art Shot Boundary Detection](https://mlsanity.com/paper/2304.06116/) (2023)
- [Large-scale, Fast and Accurate Shot Boundary Detection through Spatio-temporal Convolutional Neural Networks](https://mlsanity.com/paper/1705.03281/) (2017)

## Abstract (arXiv)

The detection of shot boundaries (hardcuts and short dissolves), sampling structure (progressive / interlaced / pulldown) and dynamic keyframes in a video are fundamental video analysis tasks which have to be done before any further high-level analysis tasks. We present a novel algorithm which does all these analysis tasks in an unified way, by utilizing a combination of inter-frame and intra-frame measures derived from the motion field and normalized cross correlation. The algorithm runs four times faster than real-time due to sparse and selective calculation of these measures. An initial evaluation furthermore shows that the proposed algorithm is extremely robust even for challenging content showing large camera or object motion, flashlights, flicker or low contrast / noise.