The effect of downsampling-upsampling strategy on foreground detection algorithms

Abstract

In video surveillance systems which incorporate stationary cameras, the first phase of movement object detection is crucial for the correct modelling of the behavior of these objects, as well as being the most complex in terms of execution time. There are many algorithms that provide a reliable and adequate segmentation mask, obtaining real-time ratios for reduced image sizes. However, due to the increased performance of camera hardware, the application of previous methods to sequences with higher resolutions (from 640x480 to 1920x1080) is not carried out in real time, compromising their use in real video surveillance systems. In this paper we propose a methodology to reduce the computational requirements of the algorithms, consisting of a reduction of the input frame and, subsequently, an interpolation of the segmentation mask of each method to recover the original frame size. In addition, the viability of this meta-model is analyzed together with the different selected algorithms, evaluating the quality of the resulting segmentation and its gain in terms of computation time.