Real-time new event detection for video streams

Online detection of video clips that present previously unseen events in a video stream is still an open challenge to date. For this online new event detection (ONED) task, existing studies mainly focus on optimizing the detection accuracy instead of the detection efficiency. As a result, it is difficult for existing systems to detect new events in real time, especially for large-scale video collections such as the video content available on the Web. In this paper, we propose several scalable techniques to improve the video processing speed of a baseline ONED system by orders of magnitude without sacrificing much detection accuracy. First, we use text features alone to filter out most of the non-new-event clips and to skip those expensive but unnecessary steps including image feature extraction and image similarity computation. Second, we use a combination of indexing and compression methods to speed up text processing. We implemented a prototype of our optimized ONED system on top of IBM's System S. The effectiveness of our techniques is evaluated on the standard TRECVID 2005 benchmark, which demonstrates that our techniques can achieve a 480-fold speedup with detection accuracy degraded less than 5%.