Vehicle Detection and Counting by Using Real Time Traffic Flux through Differential Technique and Performance Evaluation

Mohana, H. S.; Ashwathakumar, M.; Shivakumar, G.

doi:10.1109/icacc.2009.149

Cited by 15 publications

(3 citation statements)

References 7 publications

(2 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are a number of studies for estimating vehicle flow via vehicle detection and tracking. Common methods first subtract the background image, segment the vehicles in the image, and use a combination of features to track the vehicles [1,7,16,17]. These studies have shown high accuracy (90% to 96%) and can be implemented in real-time; however, the tracking algorithms such as Kalman filtering or distance of features have substantial difficulties with large object displacements common in low frame-rate cameras.…”

Section: Related Workmentioning

confidence: 99%

City-scale vehicle tracking and traffic flow estimation using low frame-rate traffic cameras

Wei

Shi

Yang

et al. 2019

Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of The

View full text Add to dashboard Cite

Vehicle flow estimation has many potential smart cities and transportation applications. Many cities have existing camera networks which broadcast image feeds; however, the resolution and frame-rate are too low for existing computer vision algorithms to accurately estimate flow. In this work, we present a computer vision and deep learning framework for vehicle tracking. We demonstrate a novel tracking pipeline which enables accurate flow estimates in a range of environments under low resolution and frame-rate constraints. We demonstrate that our system is able to track vehicles in New York City's traffic camera video feeds at 1 Hz or lower frame-rate, and produces higher traffic flow accuracy than popular open source tracking frameworks. CCS CONCEPTS • Computing methodologies → Tracking; • Software and its engineering → Real-time systems software.

show abstract

Section: Related Workmentioning

confidence: 99%

City-scale vehicle tracking and traffic flow estimation using low frame-rate traffic cameras

Wei

Shi

Yang

et al. 2019

Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of The

View full text Add to dashboard Cite

show abstract

“…Pornpanomchai et al [5] studied video vision for vehicle counting. Mohana et al [6] studied the counting of vehicles in real-time. Zhao and Wang [7] studied vehicle counting in mixed traffic zones.…”

Section: Introductionmentioning

confidence: 99%

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

Nguyen¹,

2021

EJECE

View full text Add to dashboard Cite

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%.

show abstract

“…Some studies deal with the determination of the number of vehicles in real-time traffic flux. Traffic intensity is estimated due to the presence of a vehicle in the sequence [4,5].…”

Section: Introductionmentioning

confidence: 99%

Motion clustering on video sequences using a competitive learning network

Görgünoğlu¹,

Altay²

2014

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Abstract:It is necessary to track human movements in crowded places and environments such as stations, subways, metros, and schoolyards, where security is of great importance. As a result, undesired injuries, accidents, and unusual movements can be determined and various precautionary measures can be taken against them. In this study, real-time or existing video sequences are used within the system. These video sequences are obtained from objects such as humans or vehicles, moving actively in various environments. At first, some preprocesses are made respectively, such as converting gray scale, finding the edges of the objects existing in the images, and thresholding the images. Next, motion vectors are generated by utilizing a full search algorithm. Afterwards, k-means, nearest neighbor, image subdivision, and a competitive learning network are used as clustering methods to determine dense active regions on the video sequence using these motion vectors, and then their performances are compared. It is seen that the competitive learning network significantly determines the classification of dense active regions, including motion. Moreover, the algorithms are analyzed in terms of their time performances.

show abstract

Vehicle Detection and Counting by Using Real Time Traffic Flux through Differential Technique and Performance Evaluation

Cited by 15 publications

References 7 publications

City-scale vehicle tracking and traffic flow estimation using low frame-rate traffic cameras

City-scale vehicle tracking and traffic flow estimation using low frame-rate traffic cameras

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

Motion clustering on video sequences using a competitive learning network

Contact Info

Product

Resources

About