| Pub. Date | : Jul, 2020 |
|---|---|
| Product Name | : The IUP Journal of Electrical and Electronics Engineering |
| Product Type | : Article |
| Product Code | : IJEEE10720 |
| Author Name | : Subhabrata Acharya, Pradipta Kumar Nanda |
| Availability | : YES |
| Subject/Domain | : Engineering |
| Download Format | : PDF Format |
| No. of Pages | : 16 |
The paper proposes a background modeling-based approach for the detection of foreground objects. Often, background has dominant textural behavior with some of the dynamic entities. In order to take care of both, the paper proposes two variants of Local Binary Pattern (LBP), LBP-A and LBP-EA, for background modeling. The histograms of the proposed variants of LBP learn the new video sequence to model the complex background and accurately differentiate the foreground from the background. The proposed model learning approaches have successfully been tested with different frames of PETS sequences and the efficacy of the proposed models has been found to be better than that of LBP-based modeling.
Automatic extraction of moving objects from a video sequence is a fundamental and crucial problem of many vision systems. It is gaining tremendous importance because of the need for many applications that include security-based video surveillance, traffic monitoring, military application, human detection and tracking for video teleconferencing, human-machine interface, video editing, etc. (Pietikainen et al., 2011). A common way to detect moving object is background subtraction, which is often one of the first tasks in machine vision application. The output of background subtraction is treated as the input to other higher level processes. The performance of background subtraction depends mainly on the background modeling technique used.
Background modeling, Local Binary Pattern (LBP), Video object detection