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Smartphone-Based Pedestrian’s Avoidance Behavior Recognition towards Opportunistic Road Anomaly Detection

Department of Computer and Information Sciences, Tokyo University of Agriculture and Technology, Naka-cho Koganei, 2-24-16 Tokyo, Japan
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
This paper is an extended version of our paper published in Pedestrian’s avoidance behavior recognition for road anomaly detection in the city in the Proceedings of the the ACM International Joint Conference on Pervasive and Ubiquitous Computing and ACM International Symposium on Wearable Computers, Osaka, Japan, 7–11 September 2015.
ISPRS Int. J. Geo-Inf. 2016, 5(10), 182;
Received: 24 May 2016 / Accepted: 28 September 2016 / Published: 3 October 2016
(This article belongs to the Special Issue Applications of Internet of Things)
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. View Full-Text
Keywords: road anomaly; avoidance; behavior recognition; smartphone; opportunistic sensing road anomaly; avoidance; behavior recognition; smartphone; opportunistic sensing
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Ishikawa, T.; Fujinami, K. Smartphone-Based Pedestrian’s Avoidance Behavior Recognition towards Opportunistic Road Anomaly Detection. ISPRS Int. J. Geo-Inf. 2016, 5, 182.

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