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

TL;DR

This paper introduces 2SDS, a real-time temporal segmentation algorithm that enhances CNN-based video analysis by accurately detecting scene changes and selecting optimal scene-specific results.

Contribution

The paper presents a novel real-time scene segmentation algorithm, 2SDS, that effectively integrates with CNN models to improve video stream interpretation.

Findings

2SDS achieves over 90% accuracy in scene segmentation

It effectively detects scene changes using image difference comparison

Combines with CNNs to select optimal scene results

Abstract

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%.