A Survey of Content based Video Copy Detection using Big Data
P. Karthika, P. Vidhya Saraswathi · Zenodo (CERN European Organization for Nuclear Research) · 2017
Content based video copy is the method of detecting illegally copied videos by analyzing them and comparing with original content. It extracts options from the original videos and verifies whether a duplication happens or not by looking at the extricated highlights. A state-of-the-art system of video copy detection is evaluated on VCDB to illustrate the limitations of existing techniques. The task of partial copy detection in videos aims at finding if one or more segments of a query video have (transformed) copies in a large data set. Transformations like new views are specific to 3D videos and create the copy detection even tougher. The algorithmic program realizes the 3D fake video and evaluate deep learning options learned to severally train on a distinct data sets. In this paper dynamic looking for the partial video copy detection is used to seek out a lot of segments of a query video and reference video to look the huge scale data set.