TransNet: A deep network for fast detection of common shot transitions
Published in
arXiv.org(2019)External Links:
TL
TL;DR
A simple modular convolutional neural network architecture that achieves state-of-the-art results on the RAI dataset with well above real-time inference speed even on a single mediocre GPU is presented.
Abstract
Shot boundary detection (SBD) is an important first step in many video processing applications. This paper presents a simple modular convolutional neural network architecture that achieves state-of-the-art results on the RAI dataset with well above real-time inference speed even on a single mediocre GPU. The network employs dilated convolutions and operates just on small resized frames. The training process employed randomly generated transitions using selected shots from the TRECVID IACC.3 dataset. The code and a selected trained network will be available at this https URL.
References16 items
1
Interactive Search or Sequential Browsing? A Detailed Analysis of the Video Browser Showdown 2018
2
Fast Video Shot Transition Localization with Deep Structured Models
3
On Influential Trends in Interactive Video Retrieval: Video Browser Showdown 2015–2017
4
TRECVID 2017: Evaluating Ad-hoc and Instance Video Search, Events Detection, Video Captioning and Hyperlinking
5