Nonparametric background modelling and segmentation to detect Micro Air Vehicles (MAV) using RGB-D Sensor

Abstract

A novel approach to detect Micro Air Vehicles in GPS-denied environments using an external RGB-D sensor is presented. The nonparametric background subtraction technique incorporating several innovative mechanisms allows the detection of high-speed moving MAVs by combining colour and depth information. The proposed method stores several colour and depth images as a model and then compares each pixel from a frame with the stored models to classify the pixel as background or foreground. To adapt to scene changes, once a pixel is classified as background, the system updates the model by finding and substituting the closest pixel to the camera with the current pixel. The background-model update presented uses different criteria from existing methods. Additionally, a blind-update model is added to adapt to background sudden changes. The proposed architecture is compared with existing techniques using two different MAVs and publicly available datasets. Results showing some improvements over existing methods are discussed.