Automated Traffic Surveillance System with Aerial Camera Arrays Imagery: Macroscopic Data Collection with Vehicle Tracking

Zhao, Xi-Le; Dawson, Douglas; Sarasua, Wayne; Birchfield, Stan

doi:10.1061/(asce)cp.1943-5487.0000646

Cited by 23 publications

(11 citation statements)

References 19 publications

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“…The SURF is a simple and fast algorithm to extract interest points and describe eigenvectors [30,31]. The classical SURF uses the difference of Gaussians (DoG) operator, which is inspired by the Laplacian of Gaussian (LoG) operator in scale invariant feature transform (SIFT).…”

Section: Feature Extraction Based On Surf Methodsmentioning

confidence: 99%

Research of the Vibration Source Tracking in Phase-Sensitive Optical Time-Domain Reflectometry Signals Based by Image Processing Method

Wang

2018

Algorithms

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This paper aims to improve the source tracking efficiency of distributed vibration signals generated by phase-sensitive optical time-domain reflectometry (Φ-OTDR). Considering the two dimensions (time and length) of Φ-OTDR signals, the authors saved and processed these signals as images after particle filtering. The filtering method could save 0.1% of hard drive space without sacrificing the original features of the signals. Then, an integrated feature extraction method was proposed to further process the generated image. The method combines three individual extraction methods, namely, texture feature extraction, shape feature extraction and intrinsic feature extraction. Subsequently, the signal of each frame image was recognized to track the vibration source. To verify the effect of the proposed method, several experiments were carried out to compare it with popular and traditional approaches. The results show that: Hard drive space is greatly conserved by saving the distributed vibration signals as images; the proposed particle filter is a desirable way to screen the vibration signals for monitoring; the integrated feature extraction outperforms the individual extraction methods for texture features, shape features and intrinsic features; the proposed method has a better effect than other popular integrated feature extraction methods; and, the signal source tracking method has little impact on the positioning accuracy of the vibration source. The research findings provide important insights into the source tracking of Φ-OTDR signals.

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Section: Feature Extraction Based On Surf Methodsmentioning

confidence: 99%

Research of the Vibration Source Tracking in Phase-Sensitive Optical Time-Domain Reflectometry Signals Based by Image Processing Method

Wang

2018

Algorithms

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“…Compared with the scale-invariant feature transform (SIFT), the SURF is a simple and fast algorithm to extract interest points and describe eigenvectors [38][39]. The SURF method contains five steps, namely, constructing a Hessian matrix, calculating eigenvalue, constructing a Gaussian pyramid, determining the principal direction of a feature point, locating feature points, and constructing feature descriptors.…”

Section: Extraction Of Texture Featuresmentioning

confidence: 99%

Optimal Logistics Control of an Omnichannel Supply Chain

et al. 2019

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This paper aims to find the best way to control logistics in an omnichannel supply chain (OSC). For this purpose, two steps of work were carried out around case-based reasoning (CBR). In the first step, the combined feedback which proved stability was selected to control logistics in the single node, while the variational method and the virtual siphon were combined to determine the optimal control curve. There is a linear part and a nonlinear part in the combined feedback. The new method of storing data mode is “data turning to picture”. In the second step, image features were extracted by the hybrid method of SURF-GoogLeNet and used for case matching via the grey cloud method. SURF-GoogLeNet was firstly used to update the weight proportion of the defect points in the whole image via the speeded up robust features (SURF) method and secondly to self-extract features using the GoogLeNet method. Finally, the effectiveness of the proposed methods was verified through experiments. The research findings shed new light on the management of supply chains.

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“…In this regard, the community has witnessed several innovative techniques for collecting Big Data in transport and mobility scenarios: to begin with, Zhao et al in [59] introduce a new approach capable of collecting speed, density and volume data for uninterrupted flow corridors, which is of straightforward interest for Big Data monitoring of traffic parameters over an extensive area with a single sensor. In this same line of research, the work in [60] proposes an accurate and robust vehicle trajectory data collection method based on image data captured by unmanned aerial vehicles, which can be exploited for an accurate, agile acquisition of real‐time traffic conditions of a region of interest.…”

Section: Literature Surveymentioning

confidence: 99%

Big Data for transportation and mobility: recent advances, trends and challenges

Torre-Bastida

Ser

Laña

et al. 2018

IET Intelligent Transport Systems

116

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Automated Traffic Surveillance System with Aerial Camera Arrays Imagery: Macroscopic Data Collection with Vehicle Tracking

Cited by 23 publications

References 19 publications

Research of the Vibration Source Tracking in Phase-Sensitive Optical Time-Domain Reflectometry Signals Based by Image Processing Method

Research of the Vibration Source Tracking in Phase-Sensitive Optical Time-Domain Reflectometry Signals Based by Image Processing Method

Optimal Logistics Control of an Omnichannel Supply Chain

Big Data for transportation and mobility: recent advances, trends and challenges

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