{'fraction_non_alphanumeric': 0.010025062656641603, 'fraction_numerical': 0.0, 'mean_word_length': 6.130952380952381, '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}}
Camera-based bidirectional reflectance measurement for road surface reflectivity classification

In this paper we propose a novel framework for road reflectivity classification in cluttered traffic scenarios by measuring the bidirectional reflectance distribution function of road surfaces from inside a moving vehicle. The predominant restrictions in our application are a strongly limited field of observations and a weakly defined illumination environment. To overcome these problems, we estimate the parameters of an extended Oren-Nayar model that considers the diffuse and specular behavior of real-world surfaces and extrapolate the surface reflectivity measurements to unobservable angle combinations. Model ambiguities are decreased by utilizing standardized as well as customized reflection characteristics. In contrast to existing approaches that require special measurement setups, our approach can be implemented in vision-based driver assistance systems using radiometrically uncalibrated gray value cameras and GPS information. The effectiveness of our approach is demonstrated by a successful classification of the road surface reflectance of expressway scenes with low error rates.