Improving background subtraction using Local Binary Similarity Patterns

摘要

Most of the recently published background subtraction methods can still be classified as pixel-based, as most of their analysis is still only done using pixel-by-pixel comparisons. Few others might be regarded as spatial-based (or even spatiotemporal-based) methods, as they take into account the neighborhood of each analyzed pixel. Although the latter types can be viewed as improvements in many cases, most of the methods that have been proposed so far suffer in complexity, processing speed, and/or versatility when compared to their simpler pixel-based counterparts. In this paper, we present an adaptive background subtraction method, derived from the low-cost and highly efficient ViBe method, which uses a spatiotemporal binary similarity descriptor instead of simply relying on pixel intensities as its core component. We then test this method on multiple video sequences and show that by only replacing the core component of a pixel-based method it is possible to dramatically improve its overall performance while keeping memory usage, complexity and speed at acceptable levels for online applications.