Toward Practical Crowdsourcing-Based Road Anomaly Detection With Scale-Invariant Feature
Road anomaly detection with crowdsourced sensor data has become an increasingly important field of research over the last few years. Traditional ways for road anomaly detection are either threshold-based detection techniques or feature-based detection techniques. However, road anomaly patterns from...
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doaj-83839bd3e2cd449ba27eb52724e71d7d2021-03-29T23:33:53ZengIEEEIEEE Access2169-35362019-01-017676666767810.1109/ACCESS.2019.29187548721045Toward Practical Crowdsourcing-Based Road Anomaly Detection With Scale-Invariant FeatureYuanyi Chen0https://orcid.org/0000-0003-4038-6983Mingxuan Zhou1Zengwei Zheng2Meimei Huo3Department of Computer Science and Computing, Zhejiang University City College, Hangzhou, ChinaDepartment of Computer Science and Computing, Zhejiang University City College, Hangzhou, ChinaDepartment of Computer Science and Computing, Zhejiang University City College, Hangzhou, ChinaDepartment of Computer Science and Computing, Zhejiang University City College, Hangzhou, ChinaRoad anomaly detection with crowdsourced sensor data has become an increasingly important field of research over the last few years. Traditional ways for road anomaly detection are either threshold-based detection techniques or feature-based detection techniques. However, road anomaly patterns from crowdsourcing data are often shifted in time and exhibit local distortions/noise, thus existing methods rely on the original sensor data greatly limit the accuracy of road anomaly detection. In this paper, we present a road anomaly detection model by learning scale-invariant features from the differences between small local segments of road anomaly samples. Specifically, the proposed model consists of two phases: 1) Road anomaly segmentation. The phase is designed to roughly extract road anomaly subsequence using piecewise aggregate approximation representation of sensor series data, and 2) Road anomaly detection. In this phase, we observe the differences among road anomaly classes are attributed to small local segments, then we learn scale-invariant features from these small local segments for road anomaly detection. To demonstrate the utility of our proposed model, we have performed a comprehensive experimental evaluation on two real-world datasets and one large-scale simulation dataset. The experimental results show our proposed model outperforms all baselines significantly in terms of road anomaly detection.https://ieeexplore.ieee.org/document/8721045/Road anomaly detectionmobile crowdsourcingaccelerometer readingssliding windowscale-invariant feature |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yuanyi Chen Mingxuan Zhou Zengwei Zheng Meimei Huo |
spellingShingle |
Yuanyi Chen Mingxuan Zhou Zengwei Zheng Meimei Huo Toward Practical Crowdsourcing-Based Road Anomaly Detection With Scale-Invariant Feature IEEE Access Road anomaly detection mobile crowdsourcing accelerometer readings sliding window scale-invariant feature |
author_facet |
Yuanyi Chen Mingxuan Zhou Zengwei Zheng Meimei Huo |
author_sort |
Yuanyi Chen |
title |
Toward Practical Crowdsourcing-Based Road Anomaly Detection With Scale-Invariant Feature |
title_short |
Toward Practical Crowdsourcing-Based Road Anomaly Detection With Scale-Invariant Feature |
title_full |
Toward Practical Crowdsourcing-Based Road Anomaly Detection With Scale-Invariant Feature |
title_fullStr |
Toward Practical Crowdsourcing-Based Road Anomaly Detection With Scale-Invariant Feature |
title_full_unstemmed |
Toward Practical Crowdsourcing-Based Road Anomaly Detection With Scale-Invariant Feature |
title_sort |
toward practical crowdsourcing-based road anomaly detection with scale-invariant feature |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
Road anomaly detection with crowdsourced sensor data has become an increasingly important field of research over the last few years. Traditional ways for road anomaly detection are either threshold-based detection techniques or feature-based detection techniques. However, road anomaly patterns from crowdsourcing data are often shifted in time and exhibit local distortions/noise, thus existing methods rely on the original sensor data greatly limit the accuracy of road anomaly detection. In this paper, we present a road anomaly detection model by learning scale-invariant features from the differences between small local segments of road anomaly samples. Specifically, the proposed model consists of two phases: 1) Road anomaly segmentation. The phase is designed to roughly extract road anomaly subsequence using piecewise aggregate approximation representation of sensor series data, and 2) Road anomaly detection. In this phase, we observe the differences among road anomaly classes are attributed to small local segments, then we learn scale-invariant features from these small local segments for road anomaly detection. To demonstrate the utility of our proposed model, we have performed a comprehensive experimental evaluation on two real-world datasets and one large-scale simulation dataset. The experimental results show our proposed model outperforms all baselines significantly in terms of road anomaly detection. |
topic |
Road anomaly detection mobile crowdsourcing accelerometer readings sliding window scale-invariant feature |
url |
https://ieeexplore.ieee.org/document/8721045/ |
work_keys_str_mv |
AT yuanyichen towardpracticalcrowdsourcingbasedroadanomalydetectionwithscaleinvariantfeature AT mingxuanzhou towardpracticalcrowdsourcingbasedroadanomalydetectionwithscaleinvariantfeature AT zengweizheng towardpracticalcrowdsourcingbasedroadanomalydetectionwithscaleinvariantfeature AT meimeihuo towardpracticalcrowdsourcingbasedroadanomalydetectionwithscaleinvariantfeature |
_version_ |
1724189213244522496 |