Evaluating Reorientation Strategies for Accelerometer Data from Smartphones for ITS Applications

Carlos, Manuel Ricardo; González, Luis; Martínez, Fernando; Cornejo, Raymundo

doi:10.1007/978-3-319-48799-1_45

Cited by 7 publications

(3 citation statements)

References 9 publications

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“…Seraj et al [8] decomposed acceleration data to extract feature vectors by utilizing wavelet transform. Carlos et al [9] showed that better detection effect could be obtained by features based on the standard deviation score. Alessandroni et al [13] researched the relationship between the road roughness and the vehicle speed using their own model.…”

Section: Related Workmentioning

confidence: 99%

QDetect: Time Series Querying Based Road Anomaly Detection

et al. 2020

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Road anomaly detection has attracting increasing attention in recent years due to its significant role in the public transportation of modern cities. A few methods has been proposed to detect road anomaly with inertial sensors (e.g., accelerometer and gyroscope), which usually utilize classification techniques by extracting time and frequency domain features from inertial sensor data. However, existing methods are time consuming since these methods perform on the whole datasets. In addition, few of them pay attention to the self-similarity of the data when vehicle passes over the road anomalies. In this paper, we propose QDetect, a road anomaly detection system with less data-dependency via querying and re-comparing. Specifically, QDetect consists of two phases: 1) Query filter. This phase is designed to roughly extract road anomaly segments by matching existing labelled anomalies; 2) Re-comparison on suspicious anomalies to identify their anomaly types. We have conducted comprehensive experiments on two real-world data sets, and the results show that our method outperforms some existing methods in both detection performance and running time. We expect to lay the first step to some new thoughts to the field of real-time road anomalies detection in subsequent work. INDEX TERMS Time series query, road anomaly detection, acceleration data, top-k.

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Section: Related Workmentioning

confidence: 99%

QDetect: Time Series Querying Based Road Anomaly Detection

et al. 2020

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“…Seraj et al 9 utilized wavelet transform to decompose acceleration data to extract feature vectors. Carlos et al 10 showed that the identification feature based on the standard deviation score can obtain better detection effect. El-Wakeel et al 11 used wavelet denoising method to improve the quality of low-cost MEMS sensing data and made a roadway surface disruption based on the characteristics of time domain and frequency domain information extraction of acceleration.…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, the detection accuracy is quite low. Recently, a few studies first extracted different kinds of features [7][8][9][10][11][12] and then used classification models 9,[11][12][13] such as support vector machine, k-means, or decision tree. These methods suffer a few limitations: (1) they rarely filter out the impact of normal roads on model training; (2) the data between two anomalies in the same category may be similar in time domain, but none of them take this into consideration; and (3) they often use a fixed window length, leading to the possibility to slice anomalies' data.…”

Section: Introductionmentioning

confidence: 99%

Enabling real-time road anomaly detection via mobile edge computing

Zheng

Zhou

Chen

et al. 2019

International Journal of Distributed Sensor Networks

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To discover road anomalies, a large number of detection methods have been proposed. Most of them apply classification techniques by extracting time and frequency features from the acceleration data. Existing methods are time-consuming since these methods perform on the whole datasets. In addition, few of them pay attention to the similarity of the data itself when vehicle passes over the road anomalies. In this article, we propose QF-COTE, a real-time road anomaly detection system via mobile edge computing. Specifically, QF-COTE consists of two phases: (1) Quick filter. This phase is designed to roughly extract road anomaly segments by applying random forest filter and can be performed on the edge node.(2) Road anomaly detection. In this phase, we utilize collective of transformation-based ensembles to detect road anomalies and can be performed on the cloud node. We show that our method performs clearly beyond some existing methods in both detection performance and running time. To support this conclusion, experiments are conducted based on two real-world data sets and the results are statistically analyzed. We also conduct two experiments to explore the influence of velocity and sample rate. We expect to lay the first step to some new thoughts to the field of real-time road anomalies detection in subsequent work.

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Road Environment Perception for Safe and Comfortable Driving

Guo,

Jiang,

et al. 2023

Advances in Computer Vision and Pattern Recognition

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Evaluating Reorientation Strategies for Accelerometer Data from Smartphones for ITS Applications

Cited by 7 publications

References 9 publications

QDetect: Time Series Querying Based Road Anomaly Detection

QDetect: Time Series Querying Based Road Anomaly Detection

Enabling real-time road anomaly detection via mobile edge computing

Road Environment Perception for Safe and Comfortable Driving

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