Scene Separation & Data Selection: Temporal Segmentation Algorithm for Real-Time Video Stream Analysis
It addresses video analysis inefficiencies for real-time applications, but appears incremental as it complements existing CNN models.
The paper tackles the problem of real-time video stream analysis by introducing 2SDS, a temporal segmentation algorithm that detects scene changes and selects optimal results per scene, achieving over 90% accuracy in preliminary experiments.
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%.