Video Data Extraction and Processing for Investigation of Vehicles’ Impact on the Asphalt Deformation Through the Prism of Computational Algorithms

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dc.citation.epage906
dc.citation.spage899
dc.citation.volume37
dc.contributor.authorVrtagić. Sabahudin
dc.contributor.authorSoftić, Edis
dc.contributor.authorPonjavić, Mirza
dc.contributor.authorStević, Željko
dc.contributor.authorSubotić, Marko
dc.contributor.authorGmanjunath, Aditya
dc.contributor.authorKevric, Jasmin
dc.date.accessioned2023-06-08T12:18:34Z
dc.date.available2023-06-08T12:18:34Z
dc.date.issued2020
dc.description.abstractThere are numerous algorithms and solutions for car or object detection as humanity is aiming towards the smart city solutions. Most solutions are based on counting, speed detection, traffic accidents and vehicle classification. The mentioned solutions are mostly based on high-quality videos, wide angles camera view, vehicles in motion, and are optimized for good visibility conditions intervals. A novelty of the proposed algorithm and solution is more accurate digital data extraction from video file sources generated by security cameras in Bosnia and Herzegovina from M18 roadway, but not limited only to that particular source. From the video file sources, data regarding number of vehicles, speed, traveling direction, and time intervals for the region of interest will be collected. Since finding contours approach is effective only on objects that are mobile, and because the application of this approach on traffic junctions did not yield desired results, a more specific approach of classification using a combination of Histogram of Oriented Gradients (HOG) and Support Vector Machines (Linear SVM) has shown to be more appropriate as the original source data can be used for training where the main benefit is the preservation of local second-order interactions, providing tolerance to local geometric misalignment and ability to work with small data samples. The features of the objects within a frame are extracted first by standardizing the feature variables and then computing the first order gradients of the frame. In the next stage, an encoding that remains robust to small changes while being sensitive to local frame content is produced. Finally, the HOG descriptors are generated and normalized again. In this way the channel histogram and spatial vector becomes the feature vector for the Linear SVM classifier. With the following parameters and setup system accuracy was around 85 to 95%. In the next phase, after cleaning protocols on collected data parameters, data will be used to research asphalt deformation effects.
dc.identifier.doi10.18280/ts.370603
dc.identifier.urihttps://vaseljena.ues.rs.ba/handle/123456789/310
dc.language.isoen
dc.sourceTraitement du Signal
dc.subjectHistogram of Oriented Gradients (HOG), machine learning, Support Vector Machines (SVM), video processing, asphalt deformatio
dc.titleVideo Data Extraction and Processing for Investigation of Vehicles’ Impact on the Asphalt Deformation Through the Prism of Computational Algorithms
dc.typeArticle
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Саобраћајни факултет [Научни радови] / Faculty of Transport and Traffic Engineering [Scientific papers]