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Video Anomaly Search in Crowded Scenes via Spatio-Temporal Motion Context
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第一作者: Cong Y(丛杨);Yuan JS(袁浚菘);Tang YD(唐延东)
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发表年度: 2013
期: 10
卷: 8
页: 1590-1599
摘要: Video anomaly detection plays a critical role for intelligent video surveillance. We present an abnormal video event detection system that considers both spatial and temporal contexts. To characterize the video, we first perform the spatio-temporal video segmentation and then propose a new region-based descriptor called “Motion Context,” to describe both motion and appearance information of the spatio-temporal segment. For anomaly measurements, we formulate the abnormal event detection as a matching problem, which is more robust than statistic model-based methods, especially when the training dataset is of limited size. For each testing spatio-temporal segment, we search for its best match in the training dataset, and determine how normal it is using a dynamic threshold. To speed up the search process, compact random projections are also adopted. Experiments on the benchmark dataset and comparisons with the state-of-the-art methods validate the advantages of our algorithm.
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刊物名称: IEEE Transactions on Information Forensics and Security
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