Multi camera person tracking applying a graph-cuts based foreground segmentation in a homography framework
Dejan Arsić, Atanas Lyutskanov, Gerhard Rigoll, Bogdan Kwolek · 2009
Reliable tracking of objects is an inevitable prerequisite for automated video surveillance systems. As most object detection methods, which are based on machine learning, require adequate data for the application scenario, foreground segmentation is a popular method to find possible regions of interest. These usually require a specific learning phase and adaptation over time. In this work we will present a novel approach based on graph cuts, which outperforms most standard algorithms. It is commonly agreed that occlusions can only be resolved in multi camera environments. Applying multi layer homography will enable us to robustly detect and track objects applying only foreground data, resulting in a high tracking performance.