Informedia @ TRECVID 2009: Analyzing Video Motions
journal contributionposted on 01.10.2006 by Ming-Yu Chen, Huan Li, Alexander Hauptmann
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The Informedia team participated in the tasks of high-level feature extraction and event detection in surveillance video. This year, we especially put our focus on analyzing motions in videos. We developed a robust new descriptor called MoSIFT, which explicitly encodes appearance features together with motion information. For the high-level feature detection, we trained multi-modality classifiers which include traditional static features and MoSIFT. The experimental result shows that MoSIFT has solid performance on motion related concepts and is complementary to static features. For event detection, we trained event classifiers in sliding windows using a bag-of-video-word approach. To reduce the number of false alarms, we aggregated short positive windows to favor long segmentation and applied a cascade classifier approach. The performance shows dramatic improvement over last year on the event detection task.