کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6866015 | 679603 | 2015 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Spatio-temporal context analysis within video volumes for anomalous-event detection and localization
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
In this paper, we propose an anomaly-detection approach applied for video surveillance in crowded scenes. This approach is an unsupervised statistical learning framework based on analysis of spatio-temporal video-volume configuration within video cubes. It learns global activity patterns and local salient behavior patterns via clustering and sparse coding, respectively. Upon the composition-pattern dictionary learned from normal behavior, a sparse reconstruction cost criterion is designed to detect anomalies that occur in video both globally and locally. In addition, a multiple scale analysis is employed for obtaining accurate anomaly localization, considering scale variations of abnormal events. This approach is verified on publically available anomaly-detection datasets and compared with other existing work. The experiment results demonstrate that it not only detects various anomalies more efficiently, but also locates anomalous regions more accurately.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 155, 1 May 2015, Pages 309-319
Journal: Neurocomputing - Volume 155, 1 May 2015, Pages 309-319
نویسندگان
Nannan Li, Xinyu Wu, Dan Xu, Huiwen Guo, Wei Feng,