Time–frequency analysis based robust vehicle detection using seismic sensor

Ghosh, Ripul; Akula, Aparna; Kumar, Satish; Sardana, Harish Kumar

doi:10.1016/j.jsv.2015.02.011

“…Seismic sensors (three-axis geophone and single-channel seismometer) have been used for vehicle detection by Ghost et al [ 30 ]. The authors of that work have applied time–frequency analysis methods to recognize vibrations produced by passing vehicles.…”

Section: Sensing Technologiesmentioning

confidence: 99%

“…Distance between the sensor and the vehicle detection area was about 3 m. During the experimental evaluation, the impact was considered of disturbances caused by pedestrians and other vehicles. The results presented in [ 30 ] show that the proposed solution ensures high accuracy of vehicle detection and enables vehicle classification.…”

Section: Sensing Technologiesmentioning

confidence: 99%

A Survey and Comparison of Low-Cost Sensing Technologies for Road Traffic Monitoring

Bernaś

¹

,

Płaczek

²

,

Korski

³

et al. 2018

Sensors

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This paper reviews low-cost vehicle and pedestrian detection methods and compares their accuracy. The main goal of this survey is to summarize the progress achieved to date and to help identify the sensing technologies that provide high detection accuracy and meet requirements related to cost and ease of installation. Special attention is paid to wireless battery-powered detectors of small dimensions that can be quickly and effortlessly installed alongside traffic lanes (on the side of a road or on a curb) without any additional supporting structures. The comparison of detection methods presented in this paper is based on results of experiments that were conducted with a variety of sensors in a wide range of configurations. During experiments various sensor sets were analyzed. It was shown that the detection accuracy can be significantly improved by fusing data from appropriately selected set of sensors. The experimental results reveal that accurate vehicle detection can be achieved by using sets of passive sensors. Application of active sensors was necessary to obtain satisfactory results in case of pedestrian detection.

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“…Lin et al (2014) proposed a method that incrementally updates the substances with each test frame to cope with various types of changes in scene as well as moving foreground. Ghosh et al (2015) have investigated a robust time-frequency based approach with the support of pseudo-Wigner-Ville distribution assisted Rényi entropy for motion based vehicle detection. The background subtraction method proposed by Suo and Wang (2008) have improved the detection quality of moving object using adaptive Gaussian method based update rate.…”

Section: Related Workmentioning

confidence: 99%

Histogram-based adaptive learning for background modelling: moving object detection in video surveillance

Sharma

¹

,

Yadav

²

2017

IJTMCP

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The detection of moving object in the presence of complex or cluttered background is a very critical challenge. The moving object may be a person, patient, vehicle, animal or any tissue inside body in medical domain. In this context, this work has proposed a robust background subtraction method for resolving illumination variation and motion-based problems. Initially, this work has developed a background modelling method using initial few frames in training stage. In testing stage, a foreground modelling method is investigated that is able to detect moving object in video frames. In testing stage, this work classify moving pixel with a suitable threshold and update the background using appropriate learning rate. The learning rate is updated through histogram of classified resulting frame and background model. Finally, morphological filters and image processing techniques are applied to improve the detection quality. The employed method also demonstrates how it can be improved using adaptive learning rate-based controlling scheme and the incorporation of feedback-based model updating scheme. It clearly depicts strength of proposed method in handling illumination variation problems and also eliminating moving environmental effects. This method presents significant performance in comparison with considered state-of-the-art methods.

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“…Cheng et al (2011) have developed an improved version of ViBe named as ViBe-BMRI that detects illumination change and achieves significant in segmentation of moving object with accuracy. In research of Ghosh et al (2015), a Dirichlet process Gaussinan mixture model has employed with background subtraction that estimates distribution over each pixel. The problem of over and under fitting resolved by using Bayesian method.…”

Section: Related Workmentioning

confidence: 99%

Histogram-based adaptive learning for background modelling: moving object detection in video surveillance

Sharma¹,

Yadav²

2017

IJTMCP

View full text Add to dashboard Cite

The detection of moving object in the presence of complex or cluttered background is a very critical challenge. The moving object may be a person, patient, vehicle, animal or any tissue inside body in medical domain. In this context, this work has proposed a robust background subtraction method for resolving illumination variation and motion-based problems. Initially, this work has developed a background modelling method using initial few frames in training stage. In testing stage, a foreground modelling method is investigated that is able to detect moving object in video frames. In testing stage, this work classify moving pixel with a suitable threshold and update the background using appropriate learning rate. The learning rate is updated through histogram of classified resulting frame and background model. Finally, morphological filters and image processing techniques are applied to improve the detection quality. The employed method also demonstrates how it can be improved using adaptive learning rate-based controlling scheme and the incorporation of feedback-based model updating scheme. It clearly depicts strength of proposed method in handling illumination variation problems and also eliminating moving environmental effects. This method presents significant performance in comparison with considered state-of-the-art methods.

show abstract

Time–frequency analysis based robust vehicle detection using seismic sensor

Cited by 36 publications

References 27 publications

A Survey and Comparison of Low-Cost Sensing Technologies for Road Traffic Monitoring

A Survey and Comparison of Low-Cost Sensing Technologies for Road Traffic Monitoring

Histogram-based adaptive learning for background modelling: moving object detection in video surveillance

Histogram-based adaptive learning for background modelling: moving object detection in video surveillance

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