Name
Abstract | ||
|---|---|---|
In this paper, we present a novel anomaly detection framework which integrates motion and appearance cues to detect abnormal objects and behaviors in video. For motion anomaly detection, we employ statistical histograms to model the normal motion distributions and propose a notion of \"cut-bin\" in histograms to distinguish unusual motions. For appearance anomaly detection, we develop a novel scheme based on Support Vector Data Description (SVDD), which obtains a spherically shaped boundary around the normal objects to exclude abnormal objects. The two complementary cues are finally combined to achieve more comprehensive detection results. Experimental results show that the proposed approach can effectively locate abnormal objects in multiple public video scenarios, achieving comparable performance to other state-of-the-art anomaly detection techniques. HighlightsAn algorithm integrating motion and appearance cues for video anomaly detection.Motion model uses the \"cut-bin\" to detect abnormal motions.Appearance model uses a spherical boundary to exclude unusual objects.Integration of the two cues achieves higher detection rate and fewer false alarms. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.patcog.2015.09.005 | Pattern Recognition |
Keywords | Field | DocType |
Anomaly detection,Motion model,Appearance model,Support Vector Data Description (SVDD) | Anomaly detection,Histogram,Computer vision,Pattern recognition,Support vector machine,Active appearance model,Artificial intelligence,Machine learning,Mathematics,Data description | Journal |
Volume | Issue | ISSN |
51 | C | 0031-3203 |
Citations | PageRank | References |
31 | 0.85 | 32 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ying Zhang | 1 | 163 | 25.25 |
| Huchuan Lu | 2 | 4827 | 186.26 |
| Lihe Zhang | 3 | 1372 | 38.73 |
| Xiang Ruan | 4 | 1328 | 39.49 |