EFF-ViBE: An Efficient and Improved Background Subtraction Approach based on ViBE

Abstract

Background subtraction plays an important role in intelligent video surveillance since it is one of the most used tools in motion detection. If scientific progress has enabled to develop sophisticated equipment for this task, algorithms used should be improved as well. For the past decade a background subtraction technique called ViBE is gaining the field. However, the original algorithm has two main drawbacks. The first one is ghost phenomenon which appears if the initial frame contains a moving object or in the case of a sudden change in the background situations. Secondly it fails to perform well in complicated background. This paper presents an efficient background subtraction approach based on ViBE to solve these two problems. It is based on an adaptive radius to deal with complex background, on cumulative mean and pixel counting mechanism to quickly eliminate the ghost phenomenon and to adapt to sudden change in the background model.

A vibe algorithm based on fused edge information optimization

“…The improved algorithm can effectively remove ghosting, but increases the overhead of the algorithm. In [9], an improved Vibe algorithm is proposed that introduces adaptive radius for dealing with complex backgrounds, and then eliminates ghosting using cumulative means and pixel counting. [10] improved the performance of the Vibe algorithm by fusing RGB and depth information, but this approach relies on the acquisition of RGB and depth information.…”