Name
Abstract | ||
|---|---|---|
Reference update to adapt with the dynamism of environment is one of the most challenging tasks in moving object detection for video surveillance. Different background modeling techniques have been proposed. However, most of these methods suffer from high computational cost and difficulties in determining the appropriate location as well as pixel values to update the background. In this paper, we present a new algorithm which utilizes three most recent successive frames to isolate moving edges for moving object detection. It does not require any background model. Hence, it is computationally faster and applicable for real time processing. We also introduce segment based representation of edges in the proposed method instead of traditional pixel based representation which facilitates to incorporate an efficient edge-matching algorithm to solve edge localization problem. It provides robustness against the random noise, illumination variation and quantization error. Experimental results of the proposed method are included in this paper to compare with some other standard methods that are frequently used in video surveillance. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-74819-9_62 | KES (1) |
Keywords | Field | DocType |
video surveillance,efficient edge-matching algorithm,edge segment,object detection,reference update,new algorithm,standard method,different background modeling technique,pixel value,background model,real time processing,quantization error | Object detection,Computer vision,Chamfer matching,Motion detection,Computer science,Random noise,Robustness (computer science),Video tracking,Artificial intelligence,Pixel,Quantization (signal processing) | Conference |
Volume | ISSN | Citations |
4692 | 0302-9743 | 2 |
PageRank | References | Authors |
0.40 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. Ali Akber Dewan | 1 | 79 | 9.53 |
| M. Julius Hossain | 2 | 73 | 9.50 |
| Oksam Chae | 3 | 616 | 46.52 |