Tracking of fast moving objects in real time

Kruglov, Artem; Kruglov, V. N.

doi:10.1134/s1054661816030111

Cited by 7 publications

(7 citation statements)

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“…The well-known methods performing this operation can be roughly divided into three main groups: background subtraction methods [2,3], probabilistic methods [4][5][6][12][13][14] and frame difference methods [11,19]. Each group has its own advantages and disadvantages, so it is necessary to select method or combination of methods obeyed the given task in order to achieve the optimum efficiency of the system.…”

Section: Related Workmentioning

confidence: 99%

“…The problem-solving techniques considered in the research are cross-correlation function, [10,11,19]. These methods are widely used for movement analysis in real-time surveillance and control systems.…”

Section: Related Workmentioning

confidence: 99%

“…All the observed methods can be divided into several groups according to the data source on analyzed scene. This is about features which permit the structure recovery by motion [11,16], texture and silhouettes [26], contours of the objects of interest [15] or data on scene luminosity [19,25]. Structure recovery by motion [18] involves search in the image the key points regions in the form of angles or spots [16,17], determination the correspondence between detected regions, computation of their location and forming the surface of the objects.…”

Section: Related Workmentioning

confidence: 99%

“…In the given task the magnitude and direction of the log movement is determined in real-time mode by using group of methods based on the phase correlation [19,20].…”

Section: B Contour Extraction and Parameters Estimationmentioning

confidence: 99%

“…In order to calculate this coefficient the mathematical tools of inertia moment theory [6,11,19] is implemented. Obtained sets of the diameters for each frame with a binding to the log movement are stored in the resulted log accumulator D. The accumulator D is defined as a set of ordered pairs (x,y)XY, where Y is a set of diameters and X is a set of lengths.…”

Section: B Contour Extraction and Parameters Estimationmentioning

confidence: 99%

See 4 more Smart Citations

Detection and Dimension of Moving Objects Using Single Camera Applied to the Round Timber Measurement

Chiryshev

Kruglov

Atamanova

et al. 2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

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Abstract-The paper is devoted to the problem of automatic geometry evaluation of the log moving through the conveyor. The video sequence obtained from the single camera is used as the input data. The principal restrictions of the target objects described for the given task, and the requirements to the video recording of the manufacturing process are formulated on the basis of datasets from more than .5M video images. The authors' method for the video sequence segmentation in respect to the log tracking is presented. The algorithm is based on the combination of background subtraction techniques and probabilistic methods. Next part of the paper is devoted to the log geometry estimation methods. The authors' algorithm for the log geometry structure recovery is based on the detection, isolation and approximation of log boundaries. The results of the research are implemented in the development of the conveyor-tracking system for automatic log sorting.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…In the given task the magnitude and direction of the log movement is determined in real-time mode by using group of methods based on the phase correlation [19,20].…”

Section: B Contour Extraction and Parameters Estimationmentioning

confidence: 99%

Section: B Contour Extraction and Parameters Estimationmentioning

confidence: 99%

See 3 more Smart Citations

Detection and Dimension of Moving Objects Using Single Camera Applied to the Round Timber Measurement

Chiryshev

Kruglov

Atamanova

et al. 2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

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Memory Efficient Deployment of an Optical Flow Algorithm on GPU Using OpenMP

Haggui

Tadonki

Sayadi

et al. 2019

Lecture Notes in Computer Science

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In this paper, we consider the recent set of OpenMP directives related to GPU deployment and seek an evaluation through the case of an optical flow algorithm. We start by investigating various agnostic transformations that attempt to improve memory efficiency. Our case study is the so-called Lucas-Kanade algorithm, which is typically composed of a series of convolution masks (approximation of the derivatives) followed by 2 × 2 linear systems for the optical flow vectors. Since, we are dealing with a stencil computation for each stage of the algorithm, the overhead of memory accesses together with the impact on parallel scalability are expected to be noticeable, especially with the complexity of the GPU memory system. We compare our OpenMP implementation with an OpenACC one from our previous work, both on a Quadro P5000.

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