Continuous Content-Based Copy Detection over Streaming Videos

Digital videos are increasingly adopted in various multimedia applications where they are usually broadcasted or transmitted as video streams. Continuously monitoring copies on the fast and long streaming videos is gaining attention due to its importance in content and rights management. The problem of video copies detection on video streams is complicated by two issues. First, original videos may be edited, with their frames being reordered, to avoid detection. Second, there are many concurrent video streams and for each stream, there could be many continuous video copy monitoring queries. Efficient data stream algorithms are therefore essential for processing a large number of continuous queries on video streams. In this paper, we first define video sequence similarity that is robust with respect to changes of videos, and a hash-based video sketch for efficient computation of sequence similarity. We then present a novel bit vector signature of the sketch to achieve two optimization objectives: CPU cost and memory requirement. Finally, in order to handle multiple continuous queries simultaneously, we design an index structure for the query sequences. We implemented the system and use real videos for the experimental study. Experimental results confirm the efficiency and effectiveness of our proposed techniques.