Road anomalies, such as cracks, pits and puddles, have generally been identiﬁed by citizen reports made by e-mail or telephone; however, it is difﬁcult for administrative entities to locate the anomaly for repair. An advanced smartphone-based solution that sends text and/or image reports with location information is not a long-lasting solution, because it depends on people’s active reporting. In this article, we show an opportunistic sensing-based system that uses a smartphone for road anomaly detection without any active user involvement. To detect road anomalies, we focus on pedestrians’ avoidance behaviors, which are characterized by changing azimuth patterns. Three typical avoidance behaviors are deﬁned, and random forest is chosen as the classiﬁer. Twenty-nine features are deﬁned, in which features calculated by splitting a segment into the ﬁrst half and the second half and considering the monotonicity of change were proven to be effective in recognition. Experiments were carried out under an ideal and controlled environment. Ten-fold cross-validation shows an average classiﬁcation performance with an F-measure of 0.89 for six activities. The proposed recognition method was proven to be robust against the size of obstacles, and the dependency on the storing position of a smartphone can be handled by an appropriate classiﬁer per storing position. Furthermore, an analysis implies that the classiﬁcation of data from an “unknown” person can be improved by taking into account the compatibility of a classiﬁer.
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