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

Abstract

Abstract: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.

Acoustic-sensing-based Gesture Recognition Using Hierarchical Classifier

“…In addition to calculating these features in an entire segment, we split the segment into two parts at the second local minimum in the segment, and the statistical features are calculated from the first half (FH) and second half (SH) of the segment and the entire (ALL) segment based on the findings in Ref. 18. The second local minimum was chosen as the split point due to our observation that FH and SH tend to represent the first and second strokes of two-stroke gestures, respectively, such as "4" and "X".…”

Applications of Internet of Things

“…Three papers on ITS are as follows: (1) "Vehicle positioning and speed estimation based on cellular network signals for urban roads," by Lai and Kuo [41]; (2) "A method for traffic congestion clustering judgment based on grey relational analysis," by Zhang et al [42]; and (3) "Smartphone-based pedestrian's avoidance behavior recognition towards opportunistic road anomaly detection," by Ishikawa and Fujinami [43].…”

“…Ishikawa and Fujinami from Japan in "Smartphone-based pedestrian's avoidance behavior recognition towards opportunistic road anomaly detection" used a random forest method as the classifier to analyze the azimuth patterns from smartphones for the detection of pedestrians' avoidance behaviors, and the road anomalies can be detected in accordance with the pedestrians' avoidance behaviors. In experimental environments, the practical pedestrians' avoidance behaviors were collected from 7 males and 2 females to evaluate the proposed method, and the results showed that the average accuracy of the proposed method was higher than that of other methods [43].…”

“…Therefore, the aim of this special issue is to introduce the readers a number of papers on various aspects of IoT applications. This special issue has received a total of 23 submitted papers with only 8 papers [41][42][43][44][45][46][47][48] accepted. A high rejection rate of 65.21% of this issue from the review process is to ensure that high-quality papers with significant results are selected and published.…”

Fine-grained Accelerometer-based Smartphone Carrying States Recognition during Walking

“…as software. Various sensors are embedded into the device, which allows the a system to extract a user's and/or a device's context such as engaging activity [19], [23], [26] and a person/device location [16], [24], identity of pedestrian [28], environmental conditions around a user [8], [10], [15], [27], and so on, which contributes to provide appropriate information/services to a user based on the context. According to a phone carrying survey, 17% of people determine the position of storing a mobile phone based on contextual restrictions, e.g.…”