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| Content Provider | IET Digital Library |
|---|---|
| Author | Gruenwedel, S. Petrović, N. I. Jovanov, L. ñeda, J. O. Niño Casta Pižurica, A. Philips, W. |
| Abstract | The problem of foreground detection in real-time video surveillance applications is addressed. Proposes is a framework, which is computationally cheap and has low memory requirements. It combines two simple processing blocks, both of which are essentially background subtraction algorithms. The main novelty of the approach is a combination of an autoregressive moving average filter with two background models having different adaptation speeds. The first model, having a lower adaptation speed, models long-term background and detects foreground objects by finding areas in the current frame which significantly differ from the proposed background model. The second model, with a higher adaptation speed, models the short-term background and is responsible for finding regions in the scene with a high foreground object activity. The final foreground detection is built by combining the outputs from these building blocks. The foreground obtained by the long-term modelling block is verified by the output of the short-term modelling block, i.e. only the objects exhibiting significant motion are detected as real foreground objects. The proposed method results in a very good foreground detection performance at a low computational cost. |
| Starting Page | 1143 |
| Ending Page | 1145 |
| Page Count | 3 |
| ISSN | 00135194 |
| Volume Number | 49 |
| e-ISSN | 1350911X |
| Issue Number | Issue 18, Aug (2013) |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/el/49/18 |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/el.2013.1944 |
| Journal | Electronics Letters |
| Access Restriction | Open |
| Rights Holder | © The Institution of Engineering and Technology |
| Subject Keyword | Adaptation Speed Autoregressive Moving Average Filter Autoregressive Moving Average Processes Background Subtraction Algorithm Costing Filtering Theory Foreground Detection Image Recognition Low Computational Cost Low Memory Requirements Real-time Video Surveillance Application Short-term Modelling Block Statistics System And Application Television And Video Equipment Video Surveillance |
| Content Type | Text |
| Resource Type | Article |
| Subject | Electrical and Electronic Engineering |
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