Gist: A Mobile Robotics Application of Context-Based Vision in Outdoor Environment

Abstract

We present context-based scene recognition for mobile robotics applications. Our classifier is able to differentiate outdoor scenes without temporal filtering relatively well from a variety of locations at a college campus using a set of features that together capture the "gist" of the scene. We compare the classification accuracy of a set of scenes from 1551 frames filmed outdoors along a path and dividing them to four and twelve different legs while obtaining a classifi- cation rate of 67.96 percent and 48.61 percent, respectively. We also tested the scalability of the features by comparing the classification results from the previous scenes with four legs with a longer path with eleven legs while obtaining a classification rate of 55.08 percent. In the end, some ideas are put forth to improve the theoretical strength of the gist features.