# Scene Separation & Data Selection: Temporal Segmentation Algorithm for Real-Time Video Stream Analysis

arXiv: https://arxiv.org/abs/2308.00210 · HTML: https://mlsanity.com/paper/2308.00210/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 11 references that are in the map.
- First posted on arXiv 1 Aug 2023.

## Builds on (most cited first, 5 of 5)

- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark](https://mlsanity.com/paper/1809.03327/) (2018)
- [Multiscale Spatio-Temporal Graph Neural Networks for 3D Skeleton-Based Motion Prediction](https://mlsanity.com/paper/2108.11244/) (2021)
- [Temporal 3D ConvNets: New Architecture and Transfer Learning for Video Classification](https://mlsanity.com/paper/1711.08200/) (2017)
- [Comparative Analysis of CNN-based Spatiotemporal Reasoning in Videos](https://mlsanity.com/paper/1909.05165/) (2019)

## Abstract (arXiv)

We present 2SDS (Scene Separation and Data Selection algorithm), a temporal segmentation algorithm used in real-time video stream interpretation. It complements CNN-based models to make use of temporal information in videos. 2SDS can detect the change between scenes in a video stream by com-paring the image difference between two frames. It separates a video into segments (scenes), and by combining itself with a CNN model, 2SDS can select the optimal result for each scene. In this paper, we will be discussing some basic methods and concepts behind 2SDS, as well as presenting some preliminary experiment results regarding 2SDS. During these experiments, 2SDS has achieved an overall accuracy of over 90%.