A new method for traffic density estimation based on topic model

Kaviani, Razie; Ahmadi, Parvin; Gholampour, Iman

doi:10.1109/spis.2015.7422323

Cited by 8 publications

(8 citation statements)

References 16 publications

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“…Moreover, the traffic density estimation rate "Light" of the proposed model is lower than the work [14] (99.01% compared to 100%), but the lower rate is not much, so can be remedied in the future; While the traffic density estimation rate "Medium" and "Heavy", the work [14] is much lower than the proposed model. Fig.…”

Section: Resultsmentioning

confidence: 81%

“…However, with the UCSD Traffic dataset [18], the proposed model achieves the traffic density estimation rate as shown in Table III. From the data of Table II and III, it finds that the traffic density estimation rate of the work [14] is not evenly at different density labels. With the proposed model, the traffic density estimation rate is more evenly, which shows that the proposed model is more stable.…”

Section: Resultsmentioning

confidence: 99%

“…With the UCSD Traffic dataset [17], the work [14] achieved the traffic density estimation rate shown in Table II. However, with the UCSD Traffic dataset [18], the proposed model achieves the traffic density estimation rate as shown in Table III.…”

Section: Resultsmentioning

confidence: 99%

“…The goal of this project is to introduce and present a machine learning application that aims to improve the quality of life of Singaporeans. R. Kaviani et al [14] propose a method to estimate traffic density based on Topic model, the Topic model is a quite popular method in the natural language processing field.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Novel Approach of Traffic Density Estimation Using CNNs and Computer Vision

Nguyen¹,

2021

EJECE

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In modern life, we face many problems, one of which is the increasingly serious traffic jam. The cause is the large volume of vehicles, inadequate infrastructure and unreasonable distribution, and ineffective traffic signal control. This requires finding methods to optimize traffic flow, especially during peak hours. To optimize traffic flow, it is necessary to determine the traffic density at each time in the streets and intersections. This paper proposed a novel approach to traffic density estimation using Convolutional Neural Networks (CNNs) and computer vision. The experimental results with UCSD traffic dataset show that the proposed solution achieved the worst estimation rate of 98.48% and the best estimation rate of 99.01%.

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Section: Resultsmentioning

confidence: 81%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Approach of Traffic Density Estimation Using CNNs and Computer Vision

Nguyen¹,

2021

EJECE

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“…Razie Kaviani et al [1], Parvin Ahmadi, Iman Gholampour, method for traffic density estimation using topic model as shown in figure 2.1. In this paper, a new framework for traffic density estimation based on topic model is proposed, which is an unsupervised model.…”

Section: Figure 1 Block Diagram For Traffic Density Estimation Based mentioning

confidence: 99%

Traffic Density Analysis using Image Processing

Ashwini¹,

Jaya²,

Samiksha³

et al. 2018

IJCA

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Real time traffic density prediction and analysis have recently gained popularity as compared to traditional traffic density system using CCTVs. The popularity and need of traffic monitoring at public places, industrial sector, and residential areas have supported the widespread use of real time traffic monitoring. The motion of the vehicle is one of the basic parameter for identification of the flow of traffic on roads. The traffic flow on the roads can be basically categorized into heavy, medium and low traffic. Majorly thresholds that are used to correctly classify the traffic in any frame. Background subtraction, edge detection, optical flow estimation, BLOB( Binary Large Object) detection, magnetic loops, computer vision filtration techniques, closure operation are some techniques that are combined by various researchers and used to correctly classify the nature of vehicular traffic in a frame. However, the vehicular movement"s nature is dynamic and unpredictable. For traditional techniques that are been used over years have a few challenges including the color of the road and obstacles such as shadow and illumination. The colors of majority of vehicles observed on roads are white, silver, and black. The roads also are cement based or tar-based those make them an obstacle in traditional systems. This paper presents a new blend of various studied techniques for Traffic Density Analysis.

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