Statistical Analysis of Vehicle Detection in the ITS Application for Monitoring the Traffic and Road Accident Using Internet of Things

Vadhwani, Diya Naresh; Thakor, Devendra

doi:10.1007/978-981-15-6229-7_5

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…Gamma fonksiyonu ile modifiye edilmiş dağılım fonksiyonu ile daha kısa aralıkları analiz etmeleri mümkün olabilecektir ve bu şekilde trafik hacmi çok daha büyük yol kesimlerinin analizi mümkün olabilecektir. Martolos ve Andel [14] çalışmasına benzer olarak Vadhwani ve Thakor [15] çalışmalarında düşük trafik hacmine sahip bir yol kesimi dikkate almışlar ve kullanmışlardır. Her ne kadar gerçek saha verileri bir dakikadan daha kısa olarak elde edilmişse de geleneksel poisson dağılımı ile tam sayı değerlerin analizine odaklanılmıştır.…”

Section: Sonuçunclassified

Poisson Distribution Function Modification to Model Short Time Intervals of Arrival Flow

Eriskin

2021

Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi

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Poisson dağılımı genel olarak kullanılan bir olasılık yoğunluk fonksiyonudur. Bu dağılım trafik akımının modellenebilmesi amacıyla da kullanılmaktadır. Fakat faktöriyel hesabı ile elde edilmesi sebebiyle süreksiz olasılık ailesine ait olan bu dağılımın, 1'den küçük ve katlarının hesabında kullanılması mümkün değildir. Bu çalışmada faktöriyel hesabı yerine n! değerinin Γ(n+1) eşitliğinden yararlanılarak gamma fonksiyonu kullanılmıştır. Yapılan bu dönüşüm ile süreksiz olasılık ailesine ait olan dağılım sürekli forma dönüştürülmüştür. Dönüşüm sonrası Isparta ili caddesi üzerinden akşam zirve saat dilimi sürecinde geçen akım, taşıtlar arası zaman cinsinden boşluk değerlerine göre modellenmiştir. Yapılan modelleme sonrası kısa süreli boşluk modellemelerinde gamma fonksiyonu ile dönüştürülen poisson olasılık fonksiyonu başarılı sonuç vermiştir. Ayrıca tam sayı değerlerde de her iki fonksiyonunda aynı değeri verdiği görülmüştür. Sonuç olarak modifiye edilmiş fonksiyonun kullanılabilirliği ortaya konmuştur.

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Section: Sonuçunclassified

Poisson Distribution Function Modification to Model Short Time Intervals of Arrival Flow

Eriskin

2021

Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi

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“…Furthermore,itisimportanttoshowthatautomaticvehicledetectionisindeedusefulformodern surveillance camera systems to avoid road accidents (Vadhwani, D et al,2021) and to organize traffic.So,trafficpredictionisthemaingoalofconcern.Therearetwotypesofalgorithmofobject detection:ObjectdetectionalgorithmsusingregressionincludesSSD (Liu,Wetal.,2016),YOLO andObjectdetectionalgorithmsusingregionproposalincludesFasterR-CNN (Ren,Setal.,2015). Hence,thispaperaimstoevaluatetheperformanceofthesealgorithmsinthecontextofvehicle detection.ItalsoproposedanarchitectureofMultimediaWirelessSensorNetwork(MWSN)which hastheroleofcapturingvehicleswithinatunnellocatedinthecity.Withregardtotherestofthe paper,Section2discussesrelatedworks,whichtreatalgorithmsusedforvehicledetection.Section 3providesanoverviewoftheFasterR-CNNmodel,theYOLOmodelandSSDmodel.Section4 presentsanoptimalarchitectureofMWSNthatcanbeappliedtomonitorvehicles.Section5presents theperformanceevaluationofthealgorithmsforvehicledetection.Section6concludesthepaper anddiscussesthemainresults.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Approach of Vehicle Detection Based on Deep Learning Algorithms and Wireless Sensors Networks

Nakkach

Zrelli

Ezzdine

2022

International Journal of Software Innovation

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Machine learning is applied to analyze and classify automatically images. Artificial intelligence (AI) is considered very successful in this area. Therefore, AI is exploited to evaluate the opportunities of big data and to extract value from massive and varied data sources. In order to detect any event (person, vehicle, dog, eyes, traffic, terrorist activity), ML is explored. Hence, advanced ML techniques recur to multimedia wireless sensor networks (MWSN) to detect any event in the considered area. In this work, the authors propose an enhanced architecture MWSN, which is able fly any event detection. In this context, this paper addresses the problem of vehicle detection using convolutional neural networks using a proposed architecture MWSN. Therefore, to reach this goal, the authors assess the performance of three state-of-the-art CNN algorithms, namely faster R-CNN, which is the most popular region-based algorithm; YOLO, which is known to be the fastest one; and SSD, which takes one single shot to detect multiple objects within the image.

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“…The vehicle detection includes sensor technology 4 and computer vision 5 . The sensors include induction coil, radar, laser, ultrasonic sensor etc.…”

Section: Introductionmentioning

confidence: 99%

Knowledge extraction for automatic driving control by using local correlation features

Jia¹

2021

Internet Technology Letters

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In recent years, with the rapid development of artificial intelligence technology, more and more companies have shifted their attention to the field of automatic driving. The target detection is one of the core technologies in the field of automatic driving, which can perceive the environment by using distributed sensors and affect the vehicle driving control. How to describe vehicle image is particularly important in target detection for automatic driving. In this paper, we combine edge information, HOG features and local association features to describe the targets in image for automatic driving. Then, the extracted features are used to learn a classifier for further vehicle detection. The experimental results show that the proposed method can achieve better prevision, recall, and F1‐measure than merely using edge features or HOG features. The proposed method can extract more comprehensive features for vehicle detection.

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Statistical Analysis of Vehicle Detection in the ITS Application for Monitoring the Traffic and Road Accident Using Internet of Things

Cited by 6 publications

References 12 publications

Poisson Distribution Function Modification to Model Short Time Intervals of Arrival Flow

Poisson Distribution Function Modification to Model Short Time Intervals of Arrival Flow

An Efficient Approach of Vehicle Detection Based on Deep Learning Algorithms and Wireless Sensors Networks

Knowledge extraction for automatic driving control by using local correlation features

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