A Fast Partial Video Copy Detection Using KNN and Global Feature Database
Weijun Tan, Hongwei Guo, Rushuai Liu
Abstract
Unlike in most previous partial video copy detection (PVCD) algorithms, where reference videos are scanned one by one, we treat the PVCD as a video search/retrieval problem. We propose a fast partial video copy detection framework in this paper. In this framework, all frame CNN features of the reference videos are organized in a KNN searchable database. Instead of scanning all reference videos, the query video segment does a fast KNN search in the global feature database. The returned results are used to generate a shortlist of candidate videos. A modified temporal network is then used to localize the copy segment in the candidate videos. Furthermore, We propose to use a transformer encoder to improve the CNN feature. We evaluate our algorithm on the VCDB dataset. Our benchmark F1 scores exceed state-of-the-art by a big margin. The speed of our algorithm is also improved significantly.