Moving Object Detection and Counting in Traffic with Gaussian Mixture Models and Vehicle License Plate Recognition with Prewitt Method

Karahan, Mehmet; Kurt, Hamza; Kasnakoğlu, Coşku

doi:10.1109/ismsit56059.2022.9932687

Cited by 2 publications

(2 citation statements)

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“…As a result, a group of photographs that are visually comparable to the reference samples is discovered. The Euclidean distance is used as the standard of comparison (Karahan et al, 2022;Kalyan et al, 2020;Murugan et al, 2019). DRLBP and DRLTP address the inability of LBP and LTP to distinguish between a light object and a dark backdrop, respectively.…”

Section: System Modelmentioning

confidence: 99%

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Effective Machine-Learning Based Traffic Surveillance Moving Vehicle Detection

Alanazi¹

2023

JOWUA

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The traffic surveillance is an integral part of the traffic monitoring. Automatic vehicle recognition is mostly used in efficient traffic management systems. Basic indicators of a successful traffic monitoring system are its resilience and dependability. In this study, we replace the pixel-by-pixel approach with a block-level technique, which drastically decreases the computing time and complexity by using a transform domain which naturally shortens the process's runtime. This is because discrete cosine transforms (DCT) makes it simple to extract the variations in intensities that are characteristic of many ecosystems. In most cases, the low-frequency component is where the most important information is stored inside the DCT blocks since it is less susceptible to noise. In this acquiring the edge shape feature of the detected item alongside the texture feature that provides the obvious moving top and bottom, the discriminative robust local ternary pattern (DRLTP) edge extraction is suggested which indistinct limits that improve detection efficiency. By summing the values of robust local ternary pattern LTP (RLTP) and differential LTP, we get the DRLTP, which is used for edge extraction (DLTP). RLTP is the greatest absolute value of LTP and its complement, whereas DLTP is the greatest absolute value of the difference between LTP and its complement.

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Section: System Modelmentioning

confidence: 99%

“…Recognizing licence plates and detecting and counting moving objects are also crucial tasks in traffic surveillance (Karahan et al, 2022). This research details the evolution of algorithms for tracking the location and number of moving objects, as well as for reading licence plates.…”

Section: Shi Et Al (mentioning

confidence: 99%