Motion Vector Based Features for Content Based Video Copy Detection

Kasim Tasdemir, A. Enis Çetin

Abstract: In this article, we propose a motion vector based feature set for Content Based Copy Detection (CBCD) of video clips. Motion vectors of image frames are one of the signatures of a given video. However, they are not descriptive enough when consecutive image frames are used because most vectors are too small. To overcome this problem we calculate motion vectors in a lower frame rate than the actual frame rate of the video. As a result we obtain longer vectors which form a robust parameter set representing a given video. Experimental results are presented.

External IDs:dblp:conf/icpr/TasdemirC10