Intelligent missions for MAVs: visual contexts for control, tracking and recognition

In this paper, we develop a unified vision system for small-scale aircraft that not only addresses basic flight stability and control, but also enables more intelligent missions, such as ground object recognition and moving-object tracking. The proposed system defines a framework for real-time image feature extraction, horizon detection and sky/ground segmentation, and contextual ground object detection. Multiscale Linear Discriminant Analysis (MLDA) defines the first stage of the vision system, and generates a multiscale description of images, incorporating both color and texture through a dynamic representation of image details. This representation is ideally suited for horizon detection and sky/ground segmentation of images, which we accomplish through the probabilistic representation of tree-structured belief networks (TSBN). Specifically, we propose incomplete meta TSBNs (IMTSBN) to accommodate the properties of our MLDA representation and to enhance the descriptive component of these statistical models. In the last stage of the vision processing, we seamlessly extend this probabilistic framework to perform computationally efficient detection and recognition of objects in the segmented ground region, through the idea of visual contexts. By exploiting visual contexts, we can quickly focus on candidate regions where objects of interest may be found, and then perform additional analysis for those regions only. Throughout, our approach is heavily influenced by real-time constraints and robustness to transient video noise.