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Title: Video anomaly search in crowded scenes via spatio-temporal motion context
Authors: Cong, Yang
Yuan, Junsong
Tang, Yandong
Keywords: DRNTU::Engineering::Computer science and engineering::Data
Issue Date: 2013
Source: Cong, Y., Yuan, J., & Tang, Y. (2013). Video Anomaly Search in Crowded Scenes via Spatio-Temporal Motion Context. IEEE Transactions on Information Forensics and Security, 8(10), 1590-1599.
Series/Report no.: IEEE transactions on information forensics and security
Abstract: 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 spatiotemporal video segmentation and then propose a new regionbased 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.
ISSN: 1556-6013
DOI: 10.1109/TIFS.2013.2272243
Rights: © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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