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.

External IDs:dblp:conf/wacv/TanGL22