Anomaly Detection and Cleaning of Highway Elevation Data from Google Earth Using Ensemble Empirical Mode Decomposition

Chen, Xinqiang; Li, Zhibin; Wang, Yinhai; Tang, Jinjun; Zhu, Wenbo; Shi, Chaojian; Wu, Huafeng

doi:10.1061/jtepbs.0000138

Cited by 12 publications

(12 citation statements)

References 25 publications

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“…Kim and Lee proposed a novel deep neural network model to remove AIS outliers and thus predict both medium-and long-term ship trajectory variation tendencies [23]. Similar researches can be found in [8,[24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 64%

“…We normalize the ship trajectory samples between A 1 and A 2 with the cubic spline interpolation, and, for more details, we suggest the reader to refer to [31]. e ship AIS data between A 2 and A 3 is normalized with the moving average model, and details can be found in [26].…”

Section: Ais Data Normalizationmentioning

confidence: 99%

“…To quantify the ship trajectory prediction performance, we compare the predicted AIS data with ground truth data with typical statistical measurements. Following the rule in previous studies [26,27], we employ the root mean square error (RMSE), mean absolute error (MAE), Frechet distance (FD), and average Euclidean distance (AED) to measure the prediction goodness. For any given ship trajectories, the prediction accuracy is quantified with the above-mentioned statistical indicators (see equations (13) to (16)).…”

Section: Evaluation Metricsmentioning

confidence: 99%

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Ship Trajectory Reconstruction from AIS Sensory Data via Data Quality Control and Prediction

Chen

Ling

Yang

et al. 2020

Mathematical Problems in Engineering

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Accurate ship trajectory plays an important role for maritime traffic control and management, and ship trajectory prediction with Automatic Identification System (AIS) data has attracted considerable research attentions in maritime traffic community. The raw AIS data may be contaminated by noises, which limits its usage in maritime traffic management applications in real world. To address the issue, we proposed an ensemble ship trajectory reconstruction framework combining data quality control procedure and prediction module. More specifically, the proposed framework implemented the data quality control procedure in three steps: trajectory separation, data denoising, and normalization. In greater detail, the data quality control procedure firstly identified outliers from the raw ship AIS data sample, which were further cleansed with the moving average model. Then, the denoised data were normalized into evenly distributed data series (in terms of time interval). After that, the proposed framework predicted ship trajectory with the artificial neural network. We verified the proposed model performance with two ship trajectories downloaded from public accessible AIS data base.

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Section: Introductionmentioning

confidence: 64%

Section: Ais Data Normalizationmentioning

confidence: 99%

Section: Evaluation Metricsmentioning

confidence: 99%

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Ship Trajectory Reconstruction from AIS Sensory Data via Data Quality Control and Prediction

Chen

Ling

Yang

et al. 2020

Mathematical Problems in Engineering

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“…To evaluate the proposed framework detection performance, we compare ship detection results with manually labeled ship positions (i.e., ground truth data) in each maritime image. Following the rules in the previous studies [28], two statistical indicators are employed to demonstrate the framework performance, which are recall rate ( ) and precision rate ( ). e indicator demonstrates the miss-detection performance of the proposed framework.…”

Section: Detection Goodness Measurementsmentioning

confidence: 99%

Video-Based Detection Infrastructure Enhancement for Automated Ship Recognition and Behavior Analysis

Chen

Yang

et al. 2020

Journal of Advanced Transportation

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Video-based detection infrastructure is crucial for promoting connected and autonomous shipping (CAS) development, which provides critical on-site traffic data for maritime participants. Ship behavior analysis, one of the fundamental tasks for fulfilling smart video-based detection infrastructure, has become an active topic in the CAS community. Previous studies focused on ship behavior analysis by exploring spatial-temporal information from automatic identification system (AIS) data, and less attention was paid to maritime surveillance videos. To bridge the gap, we proposed an ensemble you only look once (YOLO) framework for ship behavior analysis. First, we employed the convolutional neural network in the YOLO model to extract multi-scaled ship features from the input ship images. Second, the proposed framework generated many bounding boxes (i.e., potential ship positions) based on the object confidence level. Third, we suppressed the background bounding box interferences, and determined ship detection results with intersection over union (IOU) criterion, and thus obtained ship positions in each ship image. Fourth, we analyzed spatial-temporal ship behavior in consecutive maritime images based on kinematic ship information. The experimental results have shown that ships are accurately detected (i.e., both of the average recall and precision rate were higher than 90%) and the historical ship behaviors are successfully recognized. The proposed framework can be adaptively deployed in the connected and autonomous vehicle detection system in the automated terminal for the purpose of exploring the coupled interactions between traffic flow variation and heterogeneous detection infrastructures, and thus enhance terminal traffic network capacity and safety.

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“…where imf j (t) is the IMFs separated from X i (t), and r n (t) is the residual. The denoised trajectory is the sum of appropriate IMFs, which is determined with an energy-based IMF selection method [39]. In the signal-processing field, energy indicates the amount of stored information in the signal.…”

Section: E Trajectory Denoisingmentioning

confidence: 99%

Mixed Road User Trajectory Extraction From Moving Aerial Videos Based on Convolution Neural Network Detection

Feng

Fan

et al. 2020

IEEE Access

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Vehicle trajectory data under mixed traffic conditions provides critical information for urban traffic flow modeling and analysis. Recently, the application of unmanned aerial vehicles (UAV) creates a potential of reducing traffic video collection cost and enhances flexibility at the spatial-temporal coverage, supporting trajectory extraction in diverse environments. However, accurate vehicle detection is a challenge due to facts such as small vehicle size and inconspicuous object features in UAV videos. In addition, camera motion in UAV videos hardens the trajectory construction procedure. This research aims at proposing a novel framework for accurate vehicle trajectory construction from UAV videos under mixed traffic conditions. Firstly, a Convolution Neural Network (CNN)-based detection algorithm, named You Only Look Once (YOLO) v3, is applied to detect vehicles globally. Then an image registration method based on Shi-Tomasi corner detection is applied for camera motion compensation. Trajectory construction methods are proposed to obtain accurate vehicle trajectories based on data correlation and trajectory compensation. At last, the ensemble empirical mode decomposition (EEMD) is applied for trajectory data denoising. Our framework is tested on three aerial videos taken by an UAV on urban roads with one including intersection. The extracted vehicle trajectories are compared with manual counts. The results show that the proposed framework achieves an average Recall of 91.91% for motor vehicles, 81.98% for non-motorized vehicles and 78.13% for pedestrians in three videos.

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Anomaly Detection and Cleaning of Highway Elevation Data from Google Earth Using Ensemble Empirical Mode Decomposition

Cited by 12 publications

References 25 publications

Ship Trajectory Reconstruction from AIS Sensory Data via Data Quality Control and Prediction

Ship Trajectory Reconstruction from AIS Sensory Data via Data Quality Control and Prediction

Video-Based Detection Infrastructure Enhancement for Automated Ship Recognition and Behavior Analysis

Mixed Road User Trajectory Extraction From Moving Aerial Videos Based on Convolution Neural Network Detection

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