Video size comparison for embedded vehicle speed detection &amp; travel time estimation system by using Raspberry Pi

Iszaidy, I.; Alias, A; Ngadiran, Ruzelita; Ahmad, Robiah; Jais, M. I.; Daud, Shuhaizar

doi:10.1109/icoras.2016.7872631

Cited by 10 publications

(10 citation statements)

References 13 publications

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“…However, in the recent past, RPi based solutions for traffic flow characterization have emerged as an optimum solution because of its low cost. These solutions range from estimating vehicle count [10,11], vehicle count and classification [4,12,13] or vehicle count and speed estimation [8,9,14].…”

Section: Related Workmentioning

confidence: 99%

“…Iszaidy et al [8] proposed RPi based vehicle speed estimation solution using OpenCV and Python. Speed was estimated by tracking the vehicle's image pixel coordinates in different frames.…”

Section: 3counting and Speed Estimationmentioning

confidence: 99%

“…Though in existing literature, numerous RPi based edge computing solutions have been proposed for vehicle counting and classification [4],[ 11─13] or vehicle counting and speed estimation [8,9,14]. However, the novelty of our proposed solution is that in addition to vehicle counting, it has the capability to measure associated road-side vehicular emissions such as CO, CO2 and PM2.5.…”

Section: 1sensor Nodementioning

confidence: 99%

“…OpenCV (Open Source Computer Vision Library) is an open source and free machine learning library for image processing. OpenCV library consists of more than 2500 optimized image processing algorithms such as video capturing and encoding, object tracking and identification, and Hough transform [8]. Using OpenCV algorithms, vehicles are counted in realtime using Pi camera.…”

Section: 2software Designmentioning

confidence: 99%

See 3 more Smart Citations

Cyber physical system for vehicle counting and emission monitoring

Khan¹,

Khattak²,

Khan³

et al. 2020

IJACR

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With 25% share in world"s fossil fuel consumption, the transportation sector is contributing about 29% of worldwide greenhouse gas (GHG) emissions [1]. Furthermore, about 23% of global carbon dioxide (CO2) emissions are attributed to the transportation sector [2]. Other transportation related pollutants include nitric oxides, sulphur dioxide and particulate matter (PM) dust in urban settings. According to WHO report, transportation related ambient air pollution has resulted in 4.2 million premature deaths worldwide in 2016 [3]. About 91% of these deaths are born by low and middle-income countries [3]. With increasing urbanization, traffic related health issues will exasperate further, resulting in increased lung inflammation, cardiovascular, pulmonary and respiratory diseases [3−5].

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Section: Related Workmentioning

confidence: 99%

“…Iszaidy et al [8] proposed RPi based vehicle speed estimation solution using OpenCV and Python. Speed was estimated by tracking the vehicle's image pixel coordinates in different frames.…”

Section: 3counting and Speed Estimationmentioning

confidence: 99%

Section: 1sensor Nodementioning

confidence: 99%

Section: 2software Designmentioning

confidence: 99%

See 2 more Smart Citations

Cyber physical system for vehicle counting and emission monitoring

Khan¹,

Khattak²,

Khan³

et al. 2020

IJACR

View full text Add to dashboard Cite

show abstract

“…Mixed algorithms may be accuracy but sometimes very slow because several algorithms are combined in the same system. Xue Yuan, Lars Wilko Sommer, Shiva Kamkar and Yunsheng Zhang et al all proposed the methods through background detection and motion regions tracking to achieve the vehicle detection, recognition and positioning [4,7,15,19,25,26]. These methods are very fast and can track any motion regions in the images.…”

mentioning

confidence: 99%

Research on regional clustering and two-stage SVM method for container truck recognition

Mi¹,

Wang²,

Mi³

et al. 2019

DCDS-S

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With large-scale, integrated, intelligence for ports, many ports begin to use intelligent detection systems to make their operations more efficient. The container truck recognition and positioning system is also beginning to apply into container quayside to assist the joint operations between quay cranes and container trucks. However, the traditional vehicle detection by using motion region detection cannot recognize the type of moving object, and the traditional pattern recognition method cannot meet the requirements in real-time operation. In order to solve these problems, an algorithm fused by regional clustering and two-stage SVM classifier is proposed in this paper. The method consists of two phases, which are independently executed in two camera systems on quay cranes. In the first stage, a fast motion regional clustering algorithm is used to detect moving image patches as the truck candidate sub-windows. In the second stage, the container trucks will be recognized in these sub-windows by an optimized two-stage SVM classifier. Compared with existing traditional algorithm, experimental results in container terminal show that the fusion algorithm with regional clustering and two-stage SVM has higher efficiency and better truck recognition performance.

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Role of Artificial Intelligence and Machine Learning in scalable Internet of Things Applications

Singla

2023

2023 3rd International Conference on Advancement in Electronics &Amp; Communication Engineering (AECE)

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Video size comparison for embedded vehicle speed detection & travel time estimation system by using Raspberry Pi

Cited by 10 publications

References 13 publications

Cyber physical system for vehicle counting and emission monitoring

Cyber physical system for vehicle counting and emission monitoring

Research on regional clustering and two-stage SVM method for container truck recognition

Role of Artificial Intelligence and Machine Learning in scalable Internet of Things Applications

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