This paper introduces an alternative approach to following persons for robotic systems in a domestic environment; Flood Fill on Depth (FFOD) data. Other related work is shown: tracking using a Lucas Kanade tracker, tracking using OpenTLD (Tracking, Learning, Detecting) and tracking using a state of the art particle filter. These algorithms are all processing RGB images, while this paper suggests an alternative: image processing on depth data. The FFOD algorithm is evaluated by checking the introduced requirements and benchmarking using the Bonn Benchmark on Tracking (BoBoT). FFOD passed all critical requirements but lacks a detection system to regain a person when lost. The BoBoT benchmark resulted in a precision of 80:09%, which is almost 10% higher than a state of the art particle filter algorithm. Hereby showing that person following using depth data is an interesting approach and should be further researched.