Intelligent traffic control system using GSM technology

Ramaprasad, S S; Kumar, K N Sunil

doi:10.1109/icpcsi.2017.8391829

Cited by 14 publications

(4 citation statements)

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“…In order to improve traditional tracking methods that rely purely on object recognition, we utilize the iterative Lucas-Kanade technique with pyramids, which was invented by Bouguet [3,13]. This technique allows us to calculate the optical flow given a limited number of observed object features, notably Shi-Tomasi corners [17]. Within the realm of flow estimation, corners are characterized as small regions in the UV space that exhibit significant intensity change across the whole visual field.…”

Section: Flow-based Trackingmentioning

confidence: 99%

“…To see how the network responds when an encoder-decoder design like ResNet50 SegNet is used instead of the deep lab as a segmentation network while maintaining the same flow network as [9] and [11], wish to compare the results. CamVid [16] was used for the experiments, which is a driving scenario dataset very similar to the CityScape [17] that was used in the previous two papers.…”

Section: Introductionmentioning

confidence: 99%

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Vehicle Detection and Speed Estimation Using Semantic Segmentation with Low Latency

C K,

G K

2023

Int. j. commun. netw. inf. secur.

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Computer vision researchers are actively studying the use of video in traffic monitoring. TrafficMonitor uses a stationary calibrated camera to automatically track and classify vehicles on roadways. In practical uses like autonomous vehicles, segmenting semantic video continues to be difficult due to high-performance standards, the high cost of convolutional neural networks (CNNs), and the significant need for low latency. An effective machine learning environment will be developed to meet the performance and latency challenges outlined above. The use of deep learning architectures like SegNet and Flownet2.0 on the CamVid dataset enables this environment to conduct pixel-wise semantic segmentation of video properties while maintaining low latency. In this work, we discuss some state-of-the-art ways to estimating the speed of vehicles, locating vehicles, and tracking objects. As a result, it is ideally suited for real-world applications since it takes advantage of both SegNet and Flownet topologies. The decision network determines whether an image frame should be processed by a segmentation network or an optical flow network based on the expected confidence score. In conjunction with adaptive scheduling of the key frame approach, this technique for decision-making can help to speed up the procedure. Using the ResNet50 SegNet model, a mean IoU of "54.27 per cent" and an average fps of "19.57" were observed. Aside from decision network and adaptive key frame sequencing, it was discovered that FlowNet2.0 increased the frames processed per second to "30.19" on GPU with such a mean IoU of "47.65%". Because the GPU was utilised "47.65%" of the time, this resulted. There has been an increase in the speed of the Video semantic segmentation network without sacrificing quality, as demonstrated by this improvement in performance.

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Section: Flow-based Trackingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vehicle Detection and Speed Estimation Using Semantic Segmentation with Low Latency

C K,

G K

2023

Int. j. commun. netw. inf. secur.

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“…The model showed more foresight and initiative once it combined probabilistic reasoning with data from realtime sensors. The team used simulations to verify its method and found that it is crucial for autonomous lane change maneuvers to take into account the intentions of surrounding vehicles [12].…”

Section: Related Workmentioning

confidence: 99%

Automated Speed and Lane Change decision-making Model using Support Vector Machine

Subbaraya,

G K

2023

Int. j. commun. netw. inf. secur.

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One of the major obstacles that the auto industry must overcome is the rise of autonomous vehicles. The study of lane-changing is an important part of this problem. Previous studies on autonomous vehicle lane changes have predominantly focused on lane change path planning and path monitoring, with limited attention given to the autonomous vehicle's lane change decision-making process. This paper introduces a novel Lane Change Decision-Making Model for autonomous vehicles using the Support Vector Machine (SVM) method. The suggested model employs real-time sensor data to assess whether or not a lane change is possible, taking into account the proximity of other vehicles (cars, buses, motorbikes), and adjusting speed as necessary to ensure a seamless transition. Researching the various facets of lane changes in autonomous vehicles allows for decision-making that is grounded in utility, safety, and tolerance. The implementation of a support vector machine (SVM) technique with Bayesian parameter optimization is used to deal with the non-linearity and complexity of the process of autonomous lane change decision-making. Finally, we compare the suggested SVM model against a rule-based lane change model using the test data. The SVM-based strategy is shown to improve lane change decision-making in a comprehensive simulation exercise, which in turn improves the safety and efficiency of autonomous driving systems. The experiment also use a real vehicle to gauge the efficacy of the underlying decision-making model.

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“…Ramaprasad and Kumar have designed an intelligent traffic control system based on GSM technology at traffic junctions [47]. An IR sensor is deployed in signal junctions for the estimation of vehicles based on the traffic density across all the junctions.…”

Section: Literature Reviewmentioning

confidence: 99%

A Distributed Emergency Vehicle Transit System Using Artificial Intelligence of Things (DEVeTS-AIoT)

Mahmud

Hussain

Sarwar

et al. 2022

Wireless Communications and Mobile Computing

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The advent of the 4th industrial revolution has realized smart environments composed of cloud and fog. These have been improved to enable intelligence at the fog, where numerous, inexpensive devices communicate with each other and provide computation capabilities to solve domain-specific problems in a distributed fashion. One of these domains is smart traffic flow with a focus on emergency vehicle (EV) transit. The increase in traffic congestion in third-world countries is a hindrance in EV transit to save human lives. This paper proposes an Artificial Intelligence of Things– (AIoT–) based, distributed, EV transit system developed on Raspberry PI as a rule-based system with minimal sensors. The sensors include an infrared sensor to detect emergency light and directional microphones to detect siren. The departure direction of the EV is shared with adjacent intersections to further reduce the transit time.

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Intelligent traffic control system using GSM technology

Cited by 14 publications

References 0 publications

Vehicle Detection and Speed Estimation Using Semantic Segmentation with Low Latency

Vehicle Detection and Speed Estimation Using Semantic Segmentation with Low Latency

Automated Speed and Lane Change decision-making Model using Support Vector Machine

A Distributed Emergency Vehicle Transit System Using Artificial Intelligence of Things (DEVeTS-AIoT)

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