An Image-based Object Tracking Technique for Identification of Moving Sediment Grains on Bed Deposits

Sabzi, Ali Kh.; Hasanzadeh, Reza P. R.; Chegini, Amir

doi:10.1007/s12205-017-0576-z

Cited by 6 publications

(7 citation statements)

References 19 publications

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“…is method is featured with simple and fast operation, can obtain a more comprehensive target image, and is less affected by light, but it is not applicable in the case of large background jitter. e so-called moving target tracking is to track the moving target in the video image sequence through the use of related algorithms and then analyze and evaluate the movement and behavior of the moving target relying on the obtained position and speed parameter information [15,16]. e current moving target tracking methods mainly include feature-based, area-based, and detection-based tracking methods.…”

Section: Moving Target Detection and Trackingmentioning

confidence: 99%

[Retracted] Posture Recognition and Behavior Tracking in Swimming Motion Images under Computer Machine Vision

et al. 2021

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This study is to explore the gesture recognition and behavior tracking in swimming motion images under computer machine vision and to expand the application of moving target detection and tracking algorithms based on computer machine vision in this field. The objectives are realized by moving target detection and tracking, Gaussian mixture model, optimized correlation filtering algorithm, and Camshift tracking algorithm. Firstly, the Gaussian algorithm is introduced into target tracking and detection to reduce the filtering loss and make the acquired motion posture more accurate. Secondly, an improved kernel-related filter tracking algorithm is proposed by training multiple filters, which can clearly and accurately obtain the motion trajectory of the monitored target object. Finally, it is proposed to combine the Kalman algorithm with the Camshift algorithm for optimization, which can complete the tracking and recognition of moving targets. The experimental results show that the target tracking and detection method can obtain the movement form of the template object relatively completely, and the kernel-related filter tracking algorithm can also obtain the movement speed of the target object finely. In addition, the accuracy of Camshift tracking algorithm can reach 86.02%. Results of this study can provide reliable data support and reference for expanding the application of moving target detection and tracking methods.

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Section: Moving Target Detection and Trackingmentioning

confidence: 99%

[Retracted] Posture Recognition and Behavior Tracking in Swimming Motion Images under Computer Machine Vision

et al. 2021

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“…In addition, it has been found experimentally that a significant reduction in the grain size is associated with a lower material durability and damage resistance [14,15]. Therefore, an accurate and automatic method for tracking the collective microstructural changes that occur as a result of various external force impacts can substantially facilitate microstructural analysis and modeling, thus shortening the time to the development of new materials and leading to the better operation and maintenance of such materials during their service life [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Integrated Image Processing Toolset for Tracking Direction of Metal Grain Deformation

Dharmawan

Lee

2022

Applied Sciences

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Grain boundaries (GBs), which are among the mechanical properties of a material, are a microstructural aspect that contributes to the overall behavior of metal. A deep understanding of the behavior of the GBs’ deformation, dislocation, and fracture will encourage the rapid development of new materials and lead to the better operation and maintenance of materials during their designed lifetimes. In this study, an integrated image processing toolset is proposed to provide an expeditious approach to extracting GBs, tracking their location, and identifying their internal deformation. This toolset consists of three integrated algorithms: image stitching, grain matching, and boundary extraction. The algorithms are designed to simultaneously integrate high and low spatial resolution images for gathering high-precision boundary coordinates and effectively reconstructing a view of the entire material surface for the tracing of the grain location. This significantly reduces the time needed to acquire the dataset owing to the ability of the low spatial resolution lens to capture wider areas as the base image. The high spatial resolution lens compensates for any weakness of the base image by capturing views of specific sections, thereby increasing the observation flexibility. One application successfully described in this paper is tracking the direction of the metal grain deformation in global coordinates by stacking a specific grain before and after the deformation. This allows observers to calculate the direction of the grain deformation by comparing the overlapping areas after the material experiences a load. Ultimately, this toolset is expected to lead to further applications in terms of observing fascinating phenomena in materials science and engineering.

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“…13 Methods of monitoring could be categorised into a range of instrumentation, 13 such as float-out device and tethered buried switch, 14 pulse or radar devices, [15][16][17][18] Fiber-Bragg grating sensors, 3 buried road devices, 19,20 sound wave devices, [21][22][23] electrical conductivity probe, 24 and computer vision. [25][26][27][28][29][30] Research of the scour monitoring using computer vision approach is still lacking as compared to other approaches, such as acoustic, sensor and sonar. This is due to some limitations in computer vision that lacks robustness and it is highly sensitive to variation of lights.…”

Section: Introductionmentioning

confidence: 99%

“…Effective scour monitoring ensures up to date status of the structural integrity, provides data for immediate assessment and quick response on mitigation works 13 . Methods of monitoring could be categorised into a range of instrumentation, 13 such as float‐out device and tethered buried switch, 14 pulse or radar devices, 15–18 Fiber‐Bragg grating sensors, 3 buried road devices, 19,20 sound wave devices, 21–23 electrical conductivity probe, 24 and computer vision 25–30 …”

Section: Introductionmentioning

confidence: 99%

“…Other issues related to underwater sonar, such as dispersion, scattering and noise are not investigated. Sabzi et al 28 use a number of morphological operation algorithms in image processing and subtraction of two sequential images in order to identify and track moving sediment grains on bed deposits. The success of their solution depends on the Otsu thresholding 32 that relies largely on the brightness intensity of the grains but lacks spatial coherence about the structure of the grains, leading to undersegmentation and oversegmentation of the grains.…”

Section: Introductionmentioning

confidence: 99%

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Measuring scour level based on spatial and temporal image analyses

Mohtar

Muad

Porhemmat

et al. 2020

Struct Control Health Monit

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Scour monitoring is an important measurement process to determine the soil erosion level at the pillar of bridge. Image-based approach is attractive because it allows monitoring process to be conducted continuously without halting the flow of the water during experiment. Scour images provide abundance of information from a single source of camera sensor. However, this information may appear in different features, orientation, size and brightness. Therefore, it is important to detect and recognise features that are related to scour monitoring and filtered out irrelevant features like image noises and artefacts. This paper presents implementation of image processing techniques to extract various information from scour images. Image inpainting technique is used to separate information of scour level and scale markers into two different images. A proposed gradient of marginal histogram technique is used to detect the horizontal scale line markers and scour level. Backpropagation neural network is used to recognise scale text markers and convert the measurement of the scour level from a pixel unit into a centimetre unit. Interpolation techniques are used to connect scour points and delineate the boundary indicating the level of the scour. Time series acquisition of scour images allows observation of the temporal variation of the scour levels. Results show that the proposed approach achieved higher accuracy than existence method. This approach allows the detection of the scour level for even and uneven sediments, contributing to the high accurate results at the spatial and temporal measurements, thus potentially offering continuous scour monitoring solution.

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An Image-based Object Tracking Technique for Identification of Moving Sediment Grains on Bed Deposits

Cited by 6 publications

References 19 publications

[Retracted] Posture Recognition and Behavior Tracking in Swimming Motion Images under Computer Machine Vision

[Retracted] Posture Recognition and Behavior Tracking in Swimming Motion Images under Computer Machine Vision

Integrated Image Processing Toolset for Tracking Direction of Metal Grain Deformation

Measuring scour level based on spatial and temporal image analyses

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