Real-time Vehicle monitoring for traffic surveillance and adaptive change detection using Raspberry Pi camera module

Sorwar, Tasnim; Azad, Sabbir Bin; Hussain, Sayed Rizban; Mahmood, Azfar Isa

doi:10.1109/r10-htc.2017.8289003

Cited by 15 publications

(9 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To overcome these limitations, compute vision-based edge computing solutions are emerging as optimum solutions. However, under the constraint of compute resources, proposed solutions in existing literature can provide only traffic count [10,11], count and classification [4,12,13] or count and speed [8,9,14]. However, with incremental improvement in lowcost compute boards and computer vision algorithms, it is expected that edge computing solutions will become capable of providing a full spectrum of traffic flow parameters.…”

Section: Discussionmentioning

confidence: 99%

“…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%

“…In [12], an RPi based solution was proposed for vehicle counting and classification (either as car or bus). The proposed system had the capability of working in different lightning conditions such as day and night.…”

Section: 2counting and Classificationmentioning

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

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].

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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

“…al. [5] has another approach towards vehicle detection using raspberry pi. They made a surveillance system by offering a strategy for removing superfluous portions of the environment while also distinguishing the cars themselves.…”

Section: Related Workmentioning

confidence: 99%

Efficient and Cost-Effective Aws Cloud-Based Solution for Traffic Videos Analysis

2022

IRJMETS

View full text Add to dashboard Cite

Different research are underway, to make urbanite's life good, with the help of technology. Providing solutions to different urbanite problems such as traffic characterization, traffic congestion, identifying road accidents, and vehicle classification. The current solution offered in ITS involves two types of solution either streaming of traffic video to cloud for its computation or using edge device to compute. We made an alternate solution for the same purpose on the AWS cloud and trained our own YOLO model. The recorded video of the traffic was sent to AWS cloud for classification of vehicles. We got a detailed summary of video analysis using our trained model, its implementation using AWS in our proposed solution. The proposed model successfully classifies 1200 vehicles into 7 types in a 3 hour long video for which it was trained with a pretty good accuracy.

show abstract

“…In many cases cameras with interchangeable lenses are used, such as the increasingly popular Raspberry Pi camera used in conventional applications such as [1,2], and even pushed into more specialised applications such as [3]. Lenses are typically the most expensive parts of the imaging hardware, it is thus logical that in a low-cost application usage of cheap lenses is desirable.…”

Section: Introductionmentioning

confidence: 99%

On Bayer demosaicing impact on image sharpness in lens quality assessment

Zemčík¹,

Horák²

2022

Proceedings II of the 28st Conference STUDENT EEICT 2022: Selected Papers.

View full text Add to dashboard Cite

In this short paper an ongoing research is introduced into the methods of simple lens quality assessment for low-cost computer vision applications. Also the paradigm of colour filter arrays (CFA), CFA images and their demosaicing are described. Further the paper introduces an experiment to compare selected demosaicing algorithms and their influences on image sharpness, and therefore on the lens quality assessment. Of the selected methods the AHD (Adaptive Homogeneity-Directed) algorithm proved the most suitable for the lens evaluation.

show abstract

Real-time Vehicle monitoring for traffic surveillance and adaptive change detection using Raspberry Pi camera module

Cited by 15 publications

References 7 publications

Cyber physical system for vehicle counting and emission monitoring

Cyber physical system for vehicle counting and emission monitoring

Efficient and Cost-Effective Aws Cloud-Based Solution for Traffic Videos Analysis

On Bayer demosaicing impact on image sharpness in lens quality assessment

Contact Info

Product

Resources

About