PySceneDetect in Literature
PySceneDetect is a useful tool for statistical analysis of video. Below are links to various research articles/papers which have either used PySceneDetect as a part of their analysis, or propose more accurate detection algorithms using the current implementation as a comparison.
Online Detection of Action Start via Soft Computing for Smart City by Tian Wang, Yang Chen, Hongqiang Lv, Jing Teng, Hichem Snoussi, Fei Tao (2020)
Thesis Project: Smart Shades and Cane for The Blind by Muhammad Hashim (2020)
Movienet: a movie multilayer network model using visual and textual semantic cues by Youssef Mourchid, Benjamin Renoust, Olivier Roupin, Lê Văn, Hocine Cherifi & Mohammed El Hassouni (2019)
NLP-Enriched Automatic Video Segmentation by Mohannad AlMousa, Rachid Benlamri, Richard Khoury (2018)
Online Detection of Action Start in Untrimmed, Streaming Videos by Zheng Shou, Junting Pan, Jonathan Chan, Kazuyuki Miyazawa, Hassan Mansour, Anthony Vetro, Xavi Gir-i-Nieto, Shih-Fu Chang (2018)
Story Understanding in Video Advertisements by Keren Ye, Kyle Buettner, Adriana Kovashka (2018)
This list is only provided for academic and research purposes, and is far from an exhaustive source of the uses of PySceneDetect in literature. If you think a particular submission is relevant and should be added to this list, feel free to raise an issue with your suggestion. Publically available material is preferred, although not a requirement.
Scene Detection Methodology
Coming soon. For now, see Issue #62: Reference of paper for the methods used on Github for details.