Real-Time Vehicle Recognition and Improved Traffic Congestion Resolution

Ali, Ihtisham; Malik, Arsalan; Ahmed, Waqas; Khan, Sheraz Ali

doi:10.1109/fit.2015.48

Cited by 9 publications

(4 citation statements)

References 20 publications

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“…Their system also gets a processing rate upto 30 fps in a 1280 x 720 resolution footage. Bhaskar et al [14] also applied an image processing based method to track and identify the vehicles. Blob Detection methods and Gaussian mixture model are used to develop a unique algorithm which is able to track vehicle and recognize vehicle data at real-time.…”

Section: Related Workmentioning

confidence: 99%

An Improved Smart Traffic Signal using Computer Vision and Artificial Intelligence

Mishra*¹,

Birchha²

2019

IJRTE

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The growth in population all over the world and in particular in India causes an increase in the number of vehicles which, create complications regarding traffic jam and traffic safety. The primary solution to recover the jam condition is the expansion of capacities of roads by building new streets. However, this requires extra efforts and more time that is a costly and ineffective solution. Therefore, there is a need for alternative solution methodologies that are being implemented. Intelligent traffic monitoring is a branch of intelligent transportation systems that focuses on improving traffic signal conditions. The key goal of such an intelligent monitoring system is to improve the traffic system in a way that reduces delays. Many cities facing these delays because of the inefficient configuration of traffic light systems which are mostly fixed-cycle protocol based. Therefore, there is a profound need to improve and automate these traffic light systems. The establishment of a mixed technique of artificial intelligence (AI) and computer vision (CV) can be desirable to develop an authenticated and scalable traffic system which can aid to solve such problems. Proposed work supports the use of computer vision technology to build a resource-efficient, synchronous and automated traffic analysis. Video samples were collected from multiple areas to use in the system. The system applied and the vehicle was counted and classified into different classes. Manually and automatically annotated patterns were used for the classification. The multi-reference-line mechanism employed to find the speed of the vehicle and analyze traffic. The system makes its decision based on a number of vehicles, backwards-forward synchronous data and emergency conditions.

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Section: Related Workmentioning

confidence: 99%

An Improved Smart Traffic Signal using Computer Vision and Artificial Intelligence

Mishra*¹,

Birchha²

2019

IJRTE

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“…There are some investigations into multiple modalities for congestion sensing from more indirect measures such as weather [5] and social media [6], [7], as well as more direct observations of the traffic network, such as the use of computer vision for analysing images for signs of congestion [8], [9].…”

Section: Summary Of Prior Artmentioning

confidence: 99%

Distributed opportunistic sensing and fusion for traffic congestion detection

Notile

Harborne

Braines

et al. 2017

2017 IEEE SmartWorld, Ubiquitous Intelligence &Amp; Computing, Advanced &Amp; Trusted Computed, Scalable Computing &Amp; Commun

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Abstract-Our particular research in the Distributed Analytics and Information Science International Technology Alliance (DAIS ITA) is focused on "Anticipatory Situational Understanding for Coalitions". This paper takes the concrete example of detecting and predicting traffic congestion in the UK road transport network from existing generic sensing sources, such as real-time CCTV imagery and video, which are publicly available for this purpose. This scenario has been chosen carefully as we believe that in a typical city, all data relevant to transport network congestion information is not generally available from a single unified source, and that different organizations in the city (e.g. the weather office, the police force, the general public, etc.) have their own different sensors which can provide information potentially relevant to the traffic congestion problem. In this paper we are looking at the problem of (a) identifying congestion using cameras that, for example, the police department may have access to, and (b) fusing that with other data from other agencies in order to (c) augment any base data provided by the official transportation department feeds. By taking this coalition approach this requires using standard cameras to do different supplementary tasks like car counting, and in this paper we examine how well those tasks can be done with RNN/CNN, and other distributed machine learning processes.In this paper we provide details of an initial four-layer architecture and potential tooling to enable rapid formation of human/machine hybrid teams in this setting, with a focus on opportunistic and distributed processing of the data at the edge of the network. In future work we plan to integrate additional data-sources to further augment the core imagery data.

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“…In areas with a large number of people, it will have a great impact on people's life, work and even safety and health in the area, causing traffic delays [7]. Due to the impact of morning and evening traffic peaks, people cannot complete their work according to their expected plans, and people cannot predict their trip time [8], which directly affects more things that people have to deal with in daily life [9]. Another problem is that the vehicle will stop and start at the intersection, which directly leads to higher fuel consumption than during normal driving.…”

Section: Introductionmentioning

confidence: 99%

ITLCS Based on OpenCV Image Processing Technology

Gong

Huan

et al. 2021

J. Phys.: Conf. Ser.

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With the rapid growth of the number of motor vehicles in the city, traffic congestion is more serious every day, part of it is caused by the coding delay with the red light on, not real traffic jams, now we need a control system that can really change the traffic flow. In this paper, ITLCS (intelligent traffic signal control system) based on OpenCV image processing technology is proposed to adjust the timing of traffic signal according to road density, instead of setting a level that is balanced with other lanes, so that high-load traffic lanes can be used for a long time. The camera facing the roadway in the system takes pictures of the driving route, then takes pictures of the driving density of pedestrians and vehicles, and compares each image through processing technology, after the system is processed, the traffic light signal timing can be adjusted immediately, which greatly reduces the time spent on the inactive green light and can effectively deal with the traffic congestion problem.

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Real-Time Vehicle Recognition and Improved Traffic Congestion Resolution

Cited by 9 publications

References 20 publications

An Improved Smart Traffic Signal using Computer Vision and Artificial Intelligence

An Improved Smart Traffic Signal using Computer Vision and Artificial Intelligence

Distributed opportunistic sensing and fusion for traffic congestion detection

ITLCS Based on OpenCV Image Processing Technology

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