Moving object tracking using hybrid method

Swalaganata, Galandaru; Muniri,; Affriyenni, Yessi

doi:10.1109/icoiact.2018.8350740

Cited by 22 publications

(10 citation statements)

References 6 publications

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“…This software was used for image processing and data analysis. In image processing stage, the images were corrected to remove background values and then added to itself to increase the sharpness [8]. Then, the images were cropped to show the edges only and the process of finding the edge was conducted [9].…”

Section: Methodsmentioning

confidence: 99%

Measurement of Thermal Expansion Coefficient on Electric Cable Using X-Ray Digital Microradiography

Affriyenni¹,

Suparta²,

Swalaganata³

2018

EECSI

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Electric cable is a medium to conduct electrical energy. Expansion and contraction caused by thermal changes may result in an aging effect on the cable. This paper presents the way to observe the expansion in electrical cable due to thermal changes using the x-ray microradiography. The observed electric cables were NYA, NYAF, and NYM, each with cross-sectional areas of 1.5 mm 2 and 2.5 mm 2. The temperature was monitored using a DS18B20 sensor compiled into a microcontroller. In order to process and analyze the cables images, an ImageJ software was used. The image differences were compared based on the value of the digital image correlation. The physical analysis was carried out based on Adrian's FWHM and calculated using the regression method. The accurate structural dimension measurement using x-ray digital microradiography is about 50 μm/pixel. The average relative error measured was less than 3%.

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Section: Methodsmentioning

confidence: 99%

Measurement of Thermal Expansion Coefficient on Electric Cable Using X-Ray Digital Microradiography

Affriyenni¹,

Suparta²,

Swalaganata³

2018

EECSI

Self Cite

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“…Next, the robot arm reaches the predicted location then new parameters calculations start in order to fulfill the prediction stage. Last but not least, the robot arm moves to the predicted location and excites the refined stage to initialize the processes of adapting the predicted values alongside with the measured values of the object [8,15]. For more clarity, the refined and prediction stage algorithm is given as follows:…”

Section: Figure2 Kalman Filter Stagesmentioning

confidence: 99%

“…The location of the camera allows the robot to have free space better by more than 4 degrees. Furthermore, the information taken from the proposed image is transported and stored at the computer through the universal serial bus port in order to apply image processing operations on the image and to estimate the updated location of the target object using Kalman filter algorithm [7,8,9]. Moreover, the interfacing between the SSC-32 servo controller of lynx robot arm and the computer is done via the serial port that provides pulse width modulation PWM signals to activate the servo motors available in the arm with respect to the angles.…”

Section: Introductionmentioning

confidence: 99%

Eye in hand robot arm based automated object grasping system

Aris¹,

Mahmood²

2019

PEN

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The modern robotic systems state that the tracking methodology and the visual servoing are imperative to discover the existence of an object and excite the robot in order to manipulate the target. This paper shows a new object tracking and grasping technique in real time based on Eye in Hand visual servoing structure via a camera mounted at the end of the robot arm. The working principle of the robotic system depends mainly on the prediction based on Kalman filter method that estimates the next location of a moving object in order to specify the path of the target under the scope of the camera. Hereby, the proposed system observes the object and studies its behavior based on the pervious state in order to grasp the target at the exact position. Furthermore, the vision system implements feedback control approach to keep the extracted information of the object updated to solve the stability and the reliability issues that might be encountered. It has to be mentioned that the proposed robotic system was tested by grasping moving objects in different speeds and directions. In addition, the grasping of a stationary object was tested to confirm the practical and the theoretical results. As a final result, it can be stated that the speed of the object is directly proportional with the grasping time and vice versa.

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“…The work includes the affine transform to adapt the tracking process with scaling as well as rotation. Swalaganata et.al [5] has used a hybrid method for tracking of moving objects from video inputs, which is combination of Camshift algorithm and Kalman filter. The tracking is based on detection per frame of the video.…”

Section: Related Workmentioning

confidence: 99%

Object Recognition using Lucas-Kanade Technique and Support Vector Machine Based Classification in Video Surveillance Systems

Pillai,

2019

IJEAT

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Object recognition in video surveillance systems is the primary and most significant challenge task in the field of image processing. Video Surveillance systems provides us continuous monitoring of the objects for the enhancement of security and control. This paper presents novel approach recognizing the objects using Shi-Tomasi approach for detecting the corners of the object and then applies the Lucas-Kanade techniques to extract the features of the objects. The main objective of this paper is providing precise recognition of objects and estimation of their location from an unknown scene. Whenever the object is recognized from extracted frames of the input video the background subtraction will be applied. Then the classification of the objects into their respective categories can be achieved using support vector machine classifier by supervised learning. In case of multiple objects of different classes in a single frame, a vector containing the classes of all the detected in that frame is produced as output. The results of this work are drawn in the MATLAB tool by considering the input video dataset taken from various sources and extracting the frames from the input video for the detection then the efficiency of the proposed techniques will be measured.

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Moving object tracking using hybrid method

Cited by 22 publications

References 6 publications

Measurement of Thermal Expansion Coefficient on Electric Cable Using X-Ray Digital Microradiography

Measurement of Thermal Expansion Coefficient on Electric Cable Using X-Ray Digital Microradiography

Eye in hand robot arm based automated object grasping system

Object Recognition using Lucas-Kanade Technique and Support Vector Machine Based Classification in Video Surveillance Systems

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