Self-Adapting Fuzzy Model for Dynamic Object Detection Using RGB-D Information

This paper describes a novel method for dynamic object detection in RGB-D videos based completely on a fuzzy logic approach. The method is an original contribution because of its self-adapting fuzzy scheme that fuses color and depth information (RGB-D). The fuzzy system analyzes information related to fuzzy color and depth differences as well as fuzzy depth similitude. In order to improve the segmentation results, special cases for incomplete information in the depth pixels are treated by four new fuzzy pixel concepts; complete pixels, empty pixel, new pixel, and color pixel. The fuzzy method also solves flickering and oscillations on edge depth measurements. The proposed system also involves a self-adapting background update mechanism to avoid problems regarding changes in the video scenario sequence, and manual parameter adjustment required in other reported methods. Although the proposed method is self-adapting, it has a better or similar performance in most of the comparative cases used for analysis.