{'fraction_non_alphanumeric': 0.01951219512195122, 'fraction_numerical': 0.0, 'mean_word_length': 5.2560975609756095, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Robust extraction of shady roads for vision-based UGV navigation

This paper addresses the problem of extracting the road region in different driving environments with dynamic lighting changes. Previous approaches using Gaussian mixture models (GMM) have fixed number of models constructed from sample color data and could not keep models associated with shadows. As a result, although they work in some specific environments, they fail in other environments or in scenes with shadows. In this paper, we propose a new vision-based approach where flexible number of models are built from sample data. Those color samples are reliably collected from stereo-verified ground patches inside a pre-defined trapezoidal learning region. After model construction, models associated with shadows and highlights are detected and maintained. The advantages of this approach with respect to other techniques are that it gives more robust results and, in particular, recognizes shadows on road as drivable road surface instead of non-road.