Name
Title | ||
|---|---|---|
Spatio-temporal context analysis within video volumes for anomalous-event detection and localization |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.neucom.2014.12.064 | Neurocomputing |
Keywords | DocType | Volume |
sparse representation,bag of words | Journal | 155 |
Issue | ISSN | Citations |
C | 0925-2312 | 22 |
PageRank | References | Authors |
0.64 | 39 | 5 |