Name
Abstract | ||
|---|---|---|
In this paper we propose an approach for behavior modeling and detection of certain types of anomalous behavior. This approach consists of three basic parts. First, we propose busy-idle rates, as the behavior features, to define a behavior model for a block of pixels. Second, given a training set of normal data only, we propose spectral clustering for classifying behaviors wherein block of pixels that exhibit similar behavior models are clustered together. Then a behavior model for each cluster is obtained using the histogram of the samples. Once the behavior models are obtained, we use these models to perform anomalous behavior detection in a test video of the same scene. Experimental results on video surveillance sequences show the effectiveness and speed of proposed method. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ISTEL.2012.6483112 | Telecommunications |
Keywords | DocType | ISBN |
learning (artificial intelligence),pattern clustering,video surveillance,anomalous behavior detection,anomaly behavior detection,behavior classification,behavior modeling,behavior models,behavior recognition,busy-idle rates,normal data training set,pixel block,spectral clustering,video surveillance sequences,anomaly detection,learning artificial intelligence | Conference | 978-1-4673-2072-6 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Feizi, A. | 1 | 0 | 0.34 |
| Aghagolzadeh, A. | 2 | 9 | 2.44 |
| Seyedarabi, H. | 3 | 0 | 0.68 |